PySpark by Example¶
Setup environment¶
In [1]:
import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
spark
Out[1]:
SparkSession - in-memory
In [2]:
sc = spark.sparkContext
In [3]:
sc
Out[3]:
Downloading and preprocessing Chicago's Reported Crime Data¶
In [4]:
# !wget https://data.cityofchicago.org/api/views/ijzp-q8t2/rows.csv?accessType=DOWNLOAD
# !ls -l
In [5]:
# !mv rows.csv\?accessType\=DOWNLOAD data/reported-crimes.csv
# !ls -l
In [6]:
%time
from pyspark.sql.functions import to_timestamp,col,lit
rc = spark.read.csv('data/reported-crimes.csv',header=True).withColumn('Date',to_timestamp(col('Date'),'MM/dd/yyyy hh:mm:ss a')).filter(col('Date') <= lit('2018-11-11'))
rc.show(5)
CPU times: user 2 µs, sys: 0 ns, total: 2 µs Wall time: 4.77 µs +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ | ID|Case Number| Date| Block|IUCR| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year| Updated On|Latitude|Longitude|Location| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ |11034701| JA366925|2001-01-01 11:00:00| 016XX E 86TH PL|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| RESIDENCE| false| false|0412| 004| 8| 45| 11| null| null|2001|08/05/2017 03:50:...| null| null| null| |11227287| JB147188|2017-10-08 03:00:00| 092XX S RACINE AVE|0281|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| RESIDENCE| false| false|2222| 022| 21| 73| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227583| JB147595|2017-03-28 14:00:00| 026XX W 79TH ST|0620| BURGLARY| UNLAWFUL ENTRY| OTHER| false| false|0835| 008| 18| 70| 05| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227293| JB147230|2017-09-09 20:17:00|060XX S EBERHART AVE|0810| THEFT| OVER $500| RESIDENCE| false| false|0313| 003| 20| 42| 06| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227634| JB147599|2017-08-26 10:00:00| 001XX W RANDOLPH ST|0281|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| HOTEL/MOTEL| false| false|0122| 001| 42| 32| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ only showing top 5 rows
In [7]:
rc.cache()
rc.count()
Out[7]:
6752020
In [8]:
!du -sh data/reported-crimes.csv
1.6G data/reported-crimes.csv
Schemas¶
In [9]:
rc.printSchema()
root |-- ID: string (nullable = true) |-- Case Number: string (nullable = true) |-- Date: timestamp (nullable = true) |-- Block: string (nullable = true) |-- IUCR: string (nullable = true) |-- Primary Type: string (nullable = true) |-- Description: string (nullable = true) |-- Location Description: string (nullable = true) |-- Arrest: string (nullable = true) |-- Domestic: string (nullable = true) |-- Beat: string (nullable = true) |-- District: string (nullable = true) |-- Ward: string (nullable = true) |-- Community Area: string (nullable = true) |-- FBI Code: string (nullable = true) |-- X Coordinate: string (nullable = true) |-- Y Coordinate: string (nullable = true) |-- Year: string (nullable = true) |-- Updated On: string (nullable = true) |-- Latitude: string (nullable = true) |-- Longitude: string (nullable = true) |-- Location: string (nullable = true)
In [10]:
from pyspark.sql.types import StructType, StructField, StringType, TimestampType, BooleanType, DoubleType, IntegerType
In [11]:
rc.columns
Out[11]:
['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type', 'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat', 'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate', 'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude', 'Location']
In [12]:
schema = StructType(
[
StructField('ID', StringType(), True),
StructField('Case Number', StringType(), True),
StructField('Date', TimestampType(), True),
StructField('Block', StringType(), True),
StructField('IUCR', StringType(), True),
StructField('Primary Type', StringType(), True),
StructField('Description', StringType(), True),
StructField('Location Description', StringType(), True),
StructField('Arrest', StringType(), True),
StructField('Domestic', BooleanType(), True),
StructField('Beat', StringType(), True),
StructField('District', StringType(), True),
StructField('Ward', StringType(), True),
StructField('Community Area', StringType(), True),
StructField('FBI Code', StringType(), True),
StructField('X Coordinate', StringType(), True),
StructField('Y Coordinate', StringType(), True),
StructField('Year', IntegerType(), True),
StructField('Updated On', StringType(), True),
StructField('Latitude', DoubleType(), True),
StructField('Longitude', DoubleType(), True),
StructField('Location', StringType(), True)
]
)
In [13]:
schema
Out[13]:
StructType(List(StructField(ID,StringType,true),StructField(Case Number,StringType,true),StructField(Date,TimestampType,true),StructField(Block,StringType,true),StructField(IUCR,StringType,true),StructField(Primary Type,StringType,true),StructField(Description,StringType,true),StructField(Location Description,StringType,true),StructField(Arrest,StringType,true),StructField(Domestic,BooleanType,true),StructField(Beat,StringType,true),StructField(District,StringType,true),StructField(Ward,StringType,true),StructField(Community Area,StringType,true),StructField(FBI Code,StringType,true),StructField(X Coordinate,StringType,true),StructField(Y Coordinate,StringType,true),StructField(Year,IntegerType,true),StructField(Updated On,StringType,true),StructField(Latitude,DoubleType,true),StructField(Longitude,DoubleType,true),StructField(Location,StringType,true)))
Alternatively
In [14]:
labels = [
('ID', StringType()),
('Case Number', StringType()),
('Date', TimestampType()),
('Block', StringType()),
('IUCR', StringType()),
('Primary Type', StringType()),
('Description', StringType()),
('Location Description', StringType()),
('Arrest', StringType()),
('Domestic', BooleanType()),
('Beat', StringType()),
('District', StringType()),
('Ward', StringType()),
('Community Area', StringType()),
('FBI Code', StringType()),
('X Coordinate', StringType()),
('Y Coordinate', StringType()),
('Year', IntegerType()),
('Updated On', StringType()),
('Latitude', DoubleType()),
('Longitude', DoubleType()),
('Location', StringType())
]
In [15]:
schemaL = StructType([StructField(x[0], x[1], True) for x in labels] )
In [16]:
schemaL
Out[16]:
StructType(List(StructField(ID,StringType,true),StructField(Case Number,StringType,true),StructField(Date,TimestampType,true),StructField(Block,StringType,true),StructField(IUCR,StringType,true),StructField(Primary Type,StringType,true),StructField(Description,StringType,true),StructField(Location Description,StringType,true),StructField(Arrest,StringType,true),StructField(Domestic,BooleanType,true),StructField(Beat,StringType,true),StructField(District,StringType,true),StructField(Ward,StringType,true),StructField(Community Area,StringType,true),StructField(FBI Code,StringType,true),StructField(X Coordinate,StringType,true),StructField(Y Coordinate,StringType,true),StructField(Year,IntegerType,true),StructField(Updated On,StringType,true),StructField(Latitude,DoubleType,true),StructField(Longitude,DoubleType,true),StructField(Location,StringType,true)))
In [17]:
rcSchema = spark.read.csv('data/reported-crimes.csv',schema=schema)
rcSchema.printSchema()
root |-- ID: string (nullable = true) |-- Case Number: string (nullable = true) |-- Date: timestamp (nullable = true) |-- Block: string (nullable = true) |-- IUCR: string (nullable = true) |-- Primary Type: string (nullable = true) |-- Description: string (nullable = true) |-- Location Description: string (nullable = true) |-- Arrest: string (nullable = true) |-- Domestic: boolean (nullable = true) |-- Beat: string (nullable = true) |-- District: string (nullable = true) |-- Ward: string (nullable = true) |-- Community Area: string (nullable = true) |-- FBI Code: string (nullable = true) |-- X Coordinate: string (nullable = true) |-- Y Coordinate: string (nullable = true) |-- Year: integer (nullable = true) |-- Updated On: string (nullable = true) |-- Latitude: double (nullable = true) |-- Longitude: double (nullable = true) |-- Location: string (nullable = true)
In [18]:
rcSchemaL = spark.read.csv('data/reported-crimes.csv',schema=schemaL)
rcSchemaL.printSchema()
root |-- ID: string (nullable = true) |-- Case Number: string (nullable = true) |-- Date: timestamp (nullable = true) |-- Block: string (nullable = true) |-- IUCR: string (nullable = true) |-- Primary Type: string (nullable = true) |-- Description: string (nullable = true) |-- Location Description: string (nullable = true) |-- Arrest: string (nullable = true) |-- Domestic: boolean (nullable = true) |-- Beat: string (nullable = true) |-- District: string (nullable = true) |-- Ward: string (nullable = true) |-- Community Area: string (nullable = true) |-- FBI Code: string (nullable = true) |-- X Coordinate: string (nullable = true) |-- Y Coordinate: string (nullable = true) |-- Year: integer (nullable = true) |-- Updated On: string (nullable = true) |-- Latitude: double (nullable = true) |-- Longitude: double (nullable = true) |-- Location: string (nullable = true)
But BEWARE¶
In [19]:
rcSchema.show(5)
+----+-----------+----+-----+----+------------+-----------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------+--------+---------+--------+ | ID|Case Number|Date|Block|IUCR|Primary Type|Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year|Updated On|Latitude|Longitude|Location| +----+-----------+----+-----+----+------------+-----------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------+--------+---------+--------+ |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| +----+-----------+----+-----+----+------------+-----------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------+--------+---------+--------+ only showing top 5 rows
In [20]:
rcSchemaL.show(5)
+----+-----------+----+-----+----+------------+-----------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------+--------+---------+--------+ | ID|Case Number|Date|Block|IUCR|Primary Type|Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year|Updated On|Latitude|Longitude|Location| +----+-----------+----+-----+----+------------+-----------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------+--------+---------+--------+ |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| |null| null|null| null|null| null| null| null| null| null|null| null|null| null| null| null| null|null| null| null| null| null| +----+-----------+----+-----+----+------------+-----------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------+--------+---------+--------+ only showing top 5 rows
In [21]:
rc = spark.read.csv('data/reported-crimes.csv',header=True).withColumn('Date',to_timestamp(col('Date'),'MM/dd/yyyy hh:mm:ss a')).filter(col('Date') <= lit('2018-11-11'))
rc.show(5)
+--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ | ID|Case Number| Date| Block|IUCR| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year| Updated On|Latitude|Longitude|Location| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ |11034701| JA366925|2001-01-01 11:00:00| 016XX E 86TH PL|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| RESIDENCE| false| false|0412| 004| 8| 45| 11| null| null|2001|08/05/2017 03:50:...| null| null| null| |11227287| JB147188|2017-10-08 03:00:00| 092XX S RACINE AVE|0281|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| RESIDENCE| false| false|2222| 022| 21| 73| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227583| JB147595|2017-03-28 14:00:00| 026XX W 79TH ST|0620| BURGLARY| UNLAWFUL ENTRY| OTHER| false| false|0835| 008| 18| 70| 05| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227293| JB147230|2017-09-09 20:17:00|060XX S EBERHART AVE|0810| THEFT| OVER $500| RESIDENCE| false| false|0313| 003| 20| 42| 06| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227634| JB147599|2017-08-26 10:00:00| 001XX W RANDOLPH ST|0281|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| HOTEL/MOTEL| false| false|0122| 001| 42| 32| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ only showing top 5 rows
Working with columns¶
Display only the first 5 rows of the column name IUCR
In [22]:
rc.select('IUCR').show(5)
+----+ |IUCR| +----+ |1153| |0281| |0620| |0810| |0281| +----+ only showing top 5 rows
In [23]:
rc.select(rc.IUCR).show(5)
+----+ |IUCR| +----+ |1153| |0281| |0620| |0810| |0281| +----+ only showing top 5 rows
In [24]:
rc.select(col('IUCR')).show(5)
+----+ |IUCR| +----+ |1153| |0281| |0620| |0810| |0281| +----+ only showing top 5 rows
Display only the first 4 rows of the column names Case Number, Date and Arrest
In [25]:
rc.select('Case Number', 'Date', 'Arrest').show(4)
+-----------+-------------------+------+ |Case Number| Date|Arrest| +-----------+-------------------+------+ | JA366925|2001-01-01 11:00:00| false| | JB147188|2017-10-08 03:00:00| false| | JB147595|2017-03-28 14:00:00| false| | JB147230|2017-09-09 20:17:00| false| +-----------+-------------------+------+ only showing top 4 rows
Add a column with name One, with entries all 1s
In [26]:
from pyspark.sql.functions import lit
In [27]:
rc.withColumn('One', lit(1)).show(5)
+--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+---+ | ID|Case Number| Date| Block|IUCR| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year| Updated On|Latitude|Longitude|Location|One| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+---+ |11034701| JA366925|2001-01-01 11:00:00| 016XX E 86TH PL|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| RESIDENCE| false| false|0412| 004| 8| 45| 11| null| null|2001|08/05/2017 03:50:...| null| null| null| 1| |11227287| JB147188|2017-10-08 03:00:00| 092XX S RACINE AVE|0281|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| RESIDENCE| false| false|2222| 022| 21| 73| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| 1| |11227583| JB147595|2017-03-28 14:00:00| 026XX W 79TH ST|0620| BURGLARY| UNLAWFUL ENTRY| OTHER| false| false|0835| 008| 18| 70| 05| null| null|2017|02/11/2018 03:57:...| null| null| null| 1| |11227293| JB147230|2017-09-09 20:17:00|060XX S EBERHART AVE|0810| THEFT| OVER $500| RESIDENCE| false| false|0313| 003| 20| 42| 06| null| null|2017|02/11/2018 03:57:...| null| null| null| 1| |11227634| JB147599|2017-08-26 10:00:00| 001XX W RANDOLPH ST|0281|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| HOTEL/MOTEL| false| false|0122| 001| 42| 32| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| 1| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+---+ only showing top 5 rows
Remove the column IUCR
In [28]:
rcDropped = rc.drop('IUCR')
rcDropped.show(5)
+--------+-----------+-------------------+--------------------+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ | ID|Case Number| Date| Block| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year| Updated On|Latitude|Longitude|Location| +--------+-----------+-------------------+--------------------+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ |11034701| JA366925|2001-01-01 11:00:00| 016XX E 86TH PL| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| RESIDENCE| false| false|0412| 004| 8| 45| 11| null| null|2001|08/05/2017 03:50:...| null| null| null| |11227287| JB147188|2017-10-08 03:00:00| 092XX S RACINE AVE|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| RESIDENCE| false| false|2222| 022| 21| 73| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227583| JB147595|2017-03-28 14:00:00| 026XX W 79TH ST| BURGLARY| UNLAWFUL ENTRY| OTHER| false| false|0835| 008| 18| 70| 05| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227293| JB147230|2017-09-09 20:17:00|060XX S EBERHART AVE| THEFT| OVER $500| RESIDENCE| false| false|0313| 003| 20| 42| 06| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227634| JB147599|2017-08-26 10:00:00| 001XX W RANDOLPH ST|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| HOTEL/MOTEL| false| false|0122| 001| 42| 32| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| +--------+-----------+-------------------+--------------------+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ only showing top 5 rows
Working with rows¶
Add the reported crimes for an additional day, 12-Nov-2018, to our dataset.
In [29]:
oneDay = spark.read.format("csv").\
option("header", "true").\
load('data/reported-crimes.csv').\
withColumn('Date',to_timestamp(col('Date'),'MM/dd/yyyy hh:mm:ss a')).\
filter(col('Date') == lit('2018-11-12'))
oneDay.show()
+--------+-----------+-------------------+-------------------+----+------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+------------+-------------+--------------------+ | ID|Case Number| Date| Block|IUCR| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year| Updated On| Latitude| Longitude| Location| +--------+-----------+-------------------+-------------------+----+------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+------------+-------------+--------------------+ |11505149| JB513151|2018-11-12 00:00:00| 003XX S WHIPPLE ST|0810| THEFT| OVER $500| STREET| false| false|1124| 011| 28| 27| 06| 1156099| 1898319|2018|11/19/2018 04:22:...|41.876776356|-87.702317641|(41.876776356, -8...| |11516594| JB528186|2018-11-12 00:00:00|049XX S PRAIRIE AVE|2826| OTHER OFFENSE|HARASSMENT BY ELE...| OTHER| false| false|0224| 002| 3| 38| 26| 1178879| 1872259|2018|11/28/2018 04:14:...|41.804775828|-87.619472488|(41.804775828, -8...| |11540042| JB559262|2018-11-12 00:00:00|010XX N DEARBORN ST|1140|DECEPTIVE PRACTICE| EMBEZZLEMENT| CONVENIENCE STORE| true| false|1824| 018| 2| 8| 12| 1175747| 1907348|2018|03/16/2019 04:01:...|41.901133376|-87.629904979|(41.901133376, -8...| +--------+-----------+-------------------+-------------------+----+------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+------------+-------------+--------------------+
In [30]:
oneDayWhere = spark.read.format("csv").\
option("header", "true").\
option("inferSchema", "true").\
load('data/reported-crimes.csv').\
withColumn('Date',to_timestamp(col('Date'),'MM/dd/yyyy hh:mm:ss a')).\
where(col('Date') == lit('2018-11-12'))
oneDayWhere.show()
+--------+-----------+-------------------+-------------------+----+------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+------------+-------------+--------------------+ | ID|Case Number| Date| Block|IUCR| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year| Updated On| Latitude| Longitude| Location| +--------+-----------+-------------------+-------------------+----+------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+------------+-------------+--------------------+ |11505149| JB513151|2018-11-12 00:00:00| 003XX S WHIPPLE ST|0810| THEFT| OVER $500| STREET| false| false|1124| 11| 28| 27| 06| 1156099| 1898319|2018|11/19/2018 04:22:...|41.876776356|-87.702317641|(41.876776356, -8...| |11516594| JB528186|2018-11-12 00:00:00|049XX S PRAIRIE AVE|2826| OTHER OFFENSE|HARASSMENT BY ELE...| OTHER| false| false| 224| 2| 3| 38| 26| 1178879| 1872259|2018|11/28/2018 04:14:...|41.804775828|-87.619472488|(41.804775828, -8...| |11540042| JB559262|2018-11-12 00:00:00|010XX N DEARBORN ST|1140|DECEPTIVE PRACTICE| EMBEZZLEMENT| CONVENIENCE STORE| true| false|1824| 18| 2| 8| 12| 1175747| 1907348|2018|03/16/2019 04:01:...|41.901133376|-87.629904979|(41.901133376, -8...| +--------+-----------+-------------------+-------------------+----+------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+------------+-------------+--------------------+
In [31]:
oneDay.count()
Out[31]:
3
In [32]:
oneDay.printSchema()
root |-- ID: string (nullable = true) |-- Case Number: string (nullable = true) |-- Date: timestamp (nullable = true) |-- Block: string (nullable = true) |-- IUCR: string (nullable = true) |-- Primary Type: string (nullable = true) |-- Description: string (nullable = true) |-- Location Description: string (nullable = true) |-- Arrest: string (nullable = true) |-- Domestic: string (nullable = true) |-- Beat: string (nullable = true) |-- District: string (nullable = true) |-- Ward: string (nullable = true) |-- Community Area: string (nullable = true) |-- FBI Code: string (nullable = true) |-- X Coordinate: string (nullable = true) |-- Y Coordinate: string (nullable = true) |-- Year: string (nullable = true) |-- Updated On: string (nullable = true) |-- Latitude: string (nullable = true) |-- Longitude: string (nullable = true) |-- Location: string (nullable = true)
In [33]:
rc.printSchema()
root |-- ID: string (nullable = true) |-- Case Number: string (nullable = true) |-- Date: timestamp (nullable = true) |-- Block: string (nullable = true) |-- IUCR: string (nullable = true) |-- Primary Type: string (nullable = true) |-- Description: string (nullable = true) |-- Location Description: string (nullable = true) |-- Arrest: string (nullable = true) |-- Domestic: string (nullable = true) |-- Beat: string (nullable = true) |-- District: string (nullable = true) |-- Ward: string (nullable = true) |-- Community Area: string (nullable = true) |-- FBI Code: string (nullable = true) |-- X Coordinate: string (nullable = true) |-- Y Coordinate: string (nullable = true) |-- Year: string (nullable = true) |-- Updated On: string (nullable = true) |-- Latitude: string (nullable = true) |-- Longitude: string (nullable = true) |-- Location: string (nullable = true)
In [34]:
oneDay.count()
Out[34]:
3
In [35]:
rc.union(oneDay).show(5)
+--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ | ID|Case Number| Date| Block|IUCR| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year| Updated On|Latitude|Longitude|Location| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ |11034701| JA366925|2001-01-01 11:00:00| 016XX E 86TH PL|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| RESIDENCE| false| false|0412| 004| 8| 45| 11| null| null|2001|08/05/2017 03:50:...| null| null| null| |11227287| JB147188|2017-10-08 03:00:00| 092XX S RACINE AVE|0281|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| RESIDENCE| false| false|2222| 022| 21| 73| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227583| JB147595|2017-03-28 14:00:00| 026XX W 79TH ST|0620| BURGLARY| UNLAWFUL ENTRY| OTHER| false| false|0835| 008| 18| 70| 05| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227293| JB147230|2017-09-09 20:17:00|060XX S EBERHART AVE|0810| THEFT| OVER $500| RESIDENCE| false| false|0313| 003| 20| 42| 06| null| null|2017|02/11/2018 03:57:...| null| null| null| |11227634| JB147599|2017-08-26 10:00:00| 001XX W RANDOLPH ST|0281|CRIM SEXUAL ASSAULT| NON-AGGRAVATED| HOTEL/MOTEL| false| false|0122| 001| 42| 32| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+ only showing top 5 rows
In [36]:
rc.union(oneDay).orderBy('Date', ascending=False).show(5)
+--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+------------+-------------+--------------------+ | ID|Case Number| Date| Block|IUCR| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year| Updated On| Latitude| Longitude| Location| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+------------+-------------+--------------------+ |11505149| JB513151|2018-11-12 00:00:00| 003XX S WHIPPLE ST|0810| THEFT| OVER $500| STREET| false| false|1124| 011| 28| 27| 06| 1156099| 1898319|2018|11/19/2018 04:22:...|41.876776356|-87.702317641|(41.876776356, -8...| |11516594| JB528186|2018-11-12 00:00:00| 049XX S PRAIRIE AVE|2826| OTHER OFFENSE|HARASSMENT BY ELE...| OTHER| false| false|0224| 002| 3| 38| 26| 1178879| 1872259|2018|11/28/2018 04:14:...|41.804775828|-87.619472488|(41.804775828, -8...| |11540042| JB559262|2018-11-12 00:00:00| 010XX N DEARBORN ST|1140| DECEPTIVE PRACTICE| EMBEZZLEMENT| CONVENIENCE STORE| true| false|1824| 018| 2| 8| 12| 1175747| 1907348|2018|03/16/2019 04:01:...|41.901133376|-87.629904979|(41.901133376, -8...| |11504330| JB512058|2018-11-10 23:55:00|021XX W GREENLEAF...|0820| THEFT| $500 AND UNDER| STREET| false| false|2411| 024| 50| 2| 06| 1160583| 1946926|2018|11/17/2018 04:14:...|42.010065214|-87.684502849|(42.010065214, -8...| |11503920| JB511504|2018-11-10 23:53:00|081XX S HERMITAGE...|0910|MOTOR VEHICLE THEFT| AUTOMOBILE| APARTMENT| false| false|0614| 006| 21| 71| 07| 1166095| 1850727|2018|11/17/2018 04:14:...|41.745971025|-87.666970419|(41.745971025, -8...| +--------+-----------+-------------------+--------------------+----+-------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+------------+-------------+--------------------+ only showing top 5 rows
What are the top 10 number of reported crimes by Primary type, in descending order of occurence?
Along with a groupBy() there needs to be an aggregation along with it
In [37]:
rc.groupBy('Primary Type').count().show()
+--------------------+-------+ | Primary Type| count| +--------------------+-------+ |OFFENSE INVOLVING...| 45709| |CRIMINAL SEXUAL A...| 333| | STALKING| 3384| |PUBLIC PEACE VIOL...| 47780| | OBSCENITY| 582| |NON-CRIMINAL (SUB...| 9| | ARSON| 11154| | DOMESTIC VIOLENCE| 1| | GAMBLING| 14422| | CRIMINAL TRESPASS| 193358| | ASSAULT| 418479| | NON - CRIMINAL| 38| |LIQUOR LAW VIOLATION| 14063| | MOTOR VEHICLE THEFT| 314101| | THEFT|1418293| | BATTERY|1232001| | ROBBERY| 255566| | HOMICIDE| 9463| | RITUALISM| 23| | PUBLIC INDECENCY| 161| +--------------------+-------+ only showing top 20 rows
In [38]:
rc.groupBy('Primary Type').count().orderBy('count', ascending=False).show()
+--------------------+-------+ | Primary Type| count| +--------------------+-------+ | THEFT|1418293| | BATTERY|1232001| | CRIMINAL DAMAGE| 771399| | NARCOTICS| 711609| | OTHER OFFENSE| 418802| | ASSAULT| 418479| | BURGLARY| 388009| | MOTOR VEHICLE THEFT| 314101| | DECEPTIVE PRACTICE| 265567| | ROBBERY| 255566| | CRIMINAL TRESPASS| 193358| | WEAPONS VIOLATION| 70656| | PROSTITUTION| 68324| |PUBLIC PEACE VIOL...| 47780| |OFFENSE INVOLVING...| 45709| | CRIM SEXUAL ASSAULT| 27081| | SEX OFFENSE| 25304| |INTERFERENCE WITH...| 15135| | GAMBLING| 14422| |LIQUOR LAW VIOLATION| 14063| +--------------------+-------+ only showing top 20 rows
Challenge questions¶
What percentage of reported crimes resulted in an arrest?
In [39]:
rc.select('Arrest').distinct().show(5)
+------+ |Arrest| +------+ | false| | true| +------+
In [40]:
rc.where(col('Arrest') == 'true').count() / rc.select('Arrest').count()
Out[40]:
0.2775467193521346
What are the top 3 locations for reported crimes?
In [41]:
locArrAll = rc.groupBy('Location Description').count().orderBy('count', ascending=False).show(3)
+--------------------+-------+ |Location Description| count| +--------------------+-------+ | STREET|1770359| | RESIDENCE|1144528| | APARTMENT| 698091| +--------------------+-------+ only showing top 3 rows
In [42]:
locArrTrue = rc.select('Arrest', 'Location').where(col('Arrest') == 'true').groupBy('Location').count().orderBy('count', ascending=False)
locArrTrue.count()
Out[42]:
435860
Built-in functions¶
In [43]:
from pyspark.sql import functions
In [44]:
print(len(dir(functions)))
248
String functions¶
Display the Primary Type column in lower and upper characters, and the first 4 characters of the column
In [45]:
from pyspark.sql.functions import lower, upper, substring
In [46]:
help(substring)
Help on function substring in module pyspark.sql.functions:
substring(str, pos, len)
Substring starts at `pos` and is of length `len` when str is String type or
returns the slice of byte array that starts at `pos` in byte and is of length `len`
when str is Binary type.
.. note:: The position is not zero based, but 1 based index.
>>> df = spark.createDataFrame([('abcd',)], ['s',])
>>> df.select(substring(df.s, 1, 2).alias('s')).collect()
[Row(s='ab')]
.. versionadded:: 1.5
In [47]:
rc.select(col('Primary Type'), lower(col('Primary Type')),upper(col('Primary Type')),substring(col('Primary Type'), 1, 4)).show(5)
+-------------------+-------------------+-------------------+-----------------------------+ | Primary Type|lower(Primary Type)|upper(Primary Type)|substring(Primary Type, 1, 4)| +-------------------+-------------------+-------------------+-----------------------------+ | DECEPTIVE PRACTICE| deceptive practice| DECEPTIVE PRACTICE| DECE| |CRIM SEXUAL ASSAULT|crim sexual assault|CRIM SEXUAL ASSAULT| CRIM| | BURGLARY| burglary| BURGLARY| BURG| | THEFT| theft| THEFT| THEF| |CRIM SEXUAL ASSAULT|crim sexual assault|CRIM SEXUAL ASSAULT| CRIM| +-------------------+-------------------+-------------------+-----------------------------+ only showing top 5 rows
Numeric functions¶
Show the oldest date and the most recent date
In [48]:
from pyspark.sql.functions import min as smin, max as smax
In [49]:
rc.select(smin(col('Date')), smax(col('Date'))).show(1)
+-------------------+-------------------+ | min(Date)| max(Date)| +-------------------+-------------------+ |2001-01-01 00:00:00|2018-11-10 23:55:00| +-------------------+-------------------+
Date¶
What is 3 days earlier that the oldest date and 3 days later than the most recent date?
In [50]:
from pyspark.sql.functions import date_add, date_sub
In [51]:
help(date_add)
Help on function date_add in module pyspark.sql.functions:
date_add(start, days)
Returns the date that is `days` days after `start`
>>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
>>> df.select(date_add(df.dt, 1).alias('next_date')).collect()
[Row(next_date=datetime.date(2015, 4, 9))]
.. versionadded:: 1.5
In [52]:
rc.select(date_sub(smin(col('Date')), 3), date_add(smax(col('Date')), 3)).show(1)
+----------------------+----------------------+ |date_sub(min(Date), 3)|date_add(max(Date), 3)| +----------------------+----------------------+ | 2000-12-29| 2018-11-13| +----------------------+----------------------+
Working with dates¶
Defining dates means defining a pattern string of the form: https://docs.oracle.com/javase/8/docs/api/java/text/SimpleDateFormat.html
In [53]:
from pyspark.sql.functions import to_date, to_timestamp, lit
2019-12-25 13:30:00
In [54]:
df = spark.createDataFrame([("2019-12-25 13:30:00",)],["Christmas"])
df.show()
+-------------------+ | Christmas| +-------------------+ |2019-12-25 13:30:00| +-------------------+
In [55]:
df.select(to_date(col("Christmas"), "yyyy-MM-dd HH:mm:ss"), to_timestamp(col("Christmas"),"yyyy-MM-dd HH:mm:ss")).show()
+-------------------------------------------+------------------------------------------------+ |to_date(`Christmas`, 'yyyy-MM-dd HH:mm:ss')|to_timestamp(`Christmas`, 'yyyy-MM-dd HH:mm:ss')| +-------------------------------------------+------------------------------------------------+ | 2019-12-25| 2019-12-25 13:30:00| +-------------------------------------------+------------------------------------------------+
25/Dec/2019 13:30:00
In [56]:
df = spark.createDataFrame([("25/Dec/2019 13:30:00",)],["Christmas"])
df.show()
+--------------------+ | Christmas| +--------------------+ |25/Dec/2019 13:30:00| +--------------------+
In [57]:
df.select(to_date(col("Christmas"), "dd/MMM/yyyy HH:mm:ss"), to_timestamp(col("Christmas"),"dd/MMM/yyyy HH:mm:ss")).show()
+--------------------------------------------+-------------------------------------------------+ |to_date(`Christmas`, 'dd/MMM/yyyy HH:mm:ss')|to_timestamp(`Christmas`, 'dd/MMM/yyyy HH:mm:ss')| +--------------------------------------------+-------------------------------------------------+ | 2019-12-25| 2019-12-25 13:30:00| +--------------------------------------------+-------------------------------------------------+
12/25/2019 01:30:00 PM
In [58]:
df = spark.createDataFrame([("12/25/2019 01:30:00 PM",)],["Christmas"])
df.show(1, truncate=False)
+----------------------+ |Christmas | +----------------------+ |12/25/2019 01:30:00 PM| +----------------------+
In [59]:
df.select(to_date(col("Christmas"), "MM/dd/yyyy hh:mm:ss aa"), to_timestamp(col("Christmas"),"MM/dd/yyyy hh:mm:ss aa")).show()
+----------------------------------------------+---------------------------------------------------+ |to_date(`Christmas`, 'MM/dd/yyyy hh:mm:ss aa')|to_timestamp(`Christmas`, 'MM/dd/yyyy hh:mm:ss aa')| +----------------------------------------------+---------------------------------------------------+ | 2019-12-25| 2019-12-25 13:30:00| +----------------------------------------------+---------------------------------------------------+
In [60]:
nrc = spark.read.csv("data/reported-crimes.csv", header=True)
nrc.show(5, truncate=False)
+--------+-----------+----------------------+--------------------+----+-------------------+-----------------------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------------------+--------+---------+--------+ |ID |Case Number|Date |Block |IUCR|Primary Type |Description |Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year|Updated On |Latitude|Longitude|Location| +--------+-----------+----------------------+--------------------+----+-------------------+-----------------------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------------------+--------+---------+--------+ |11034701|JA366925 |01/01/2001 11:00:00 AM|016XX E 86TH PL |1153|DECEPTIVE PRACTICE |FINANCIAL IDENTITY THEFT OVER $ 300|RESIDENCE |false |false |0412|004 |8 |45 |11 |null |null |2001|08/05/2017 03:50:08 PM|null |null |null | |11227287|JB147188 |10/08/2017 03:00:00 AM|092XX S RACINE AVE |0281|CRIM SEXUAL ASSAULT|NON-AGGRAVATED |RESIDENCE |false |false |2222|022 |21 |73 |02 |null |null |2017|02/11/2018 03:57:41 PM|null |null |null | |11227583|JB147595 |03/28/2017 02:00:00 PM|026XX W 79TH ST |0620|BURGLARY |UNLAWFUL ENTRY |OTHER |false |false |0835|008 |18 |70 |05 |null |null |2017|02/11/2018 03:57:41 PM|null |null |null | |11227293|JB147230 |09/09/2017 08:17:00 PM|060XX S EBERHART AVE|0810|THEFT |OVER $500 |RESIDENCE |false |false |0313|003 |20 |42 |06 |null |null |2017|02/11/2018 03:57:41 PM|null |null |null | |11227634|JB147599 |08/26/2017 10:00:00 AM|001XX W RANDOLPH ST |0281|CRIM SEXUAL ASSAULT|NON-AGGRAVATED |HOTEL/MOTEL |false |false |0122|001 |42 |32 |02 |null |null |2017|02/11/2018 03:57:41 PM|null |null |null | +--------+-----------+----------------------+--------------------+----+-------------------+-----------------------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------------------+--------+---------+--------+ only showing top 5 rows
UDFs (User-Defined-Functions)¶
You won't be surprised to hear that you can also create your own functions, and these functions will be applied across your data row by row. Now, there's a really good reason why we focused on the built-in functions first before looking at these UDFs, or user-defined functions. This is because there are performance considerations that we need to be aware of. Now, once you create your function, you need to register it with Spark for it to be used on all the worker nodes. It doesn't matter what language you've used, so that's if your code is in Python, Scala, or any other supported language, the next thing Spark needs to do is to serialize the function on the driver and then send it out to all of the executor processes. When you use the function and it's written in Scala or Java, you can use it within the Java virtual machine, or JVM. This means there's little performance penalty. You still don't get the full benefits that Spark has for the built-in functions that we looked at earlier, but there isn't a bit hit on the performance. Things get a lot more interesting when it comes to Python. Spark will start a Python process on the worker, so as usual, the first step is to get the function serialized and sent to the worker nodes. Spark then has to start the Python process and send the data across. So in our case, it'll take whatever data set we're using and apply the function row by row on the data set, and then returns these row operations to the JVM in Spark. Now remember that earlier in the course, when we were looking at Spark sessions, we said that PySpark is actually a wrapper around the spark core. When you start your Spark session in Python, in the background, PySpark uses Py4J to launch a JVM and create a JavaSparkContext. So the real performance hit is in serializing the data to Python. You can imagine that this will be an expensive computation. But the bigger challenge is that when the data enters the Python process, then Spark doesn't have any control over the memory of the worker. So you can easily end up in a situation where a worker fails, simply because both Python and the JVM are competing for memory for that worker node. So basically, as long as we avoid all kind of Python user-defined functions, a PySpark program will be approximately as fast as a Spark program based on Scala. So what are our options around this performance bottleneck? Well, one option is, if you have to write your function, write it in Scala or Java. We can still access and use this function from Python. Wes McKinney, the creator of Pandas has been working on the general problem of accessing data frames from different programming languages in the Apache Arrow project. This tries to standardize the way data in columns is stored in memory, so you don't have this object translation by serialization and de-serialization anymore. This should drastically speed up our Python UDFs. Your preference should always be to use the built-in Spark functions whenever possible.
# Compatibiliy Setting for PyArrow >= 0.15.0 and Spark 2.3.x, 2.4.x
# Since Arrow 0.15.0, a change in the binary IPC format requires an environment variable to be
# compatible with previous versions of Arrow <= 0.14.1. This is only necessary to do for PySpark users
# with versions 2.3.x and 2.4.x that have manually upgraded PyArrow to 0.15.0. The following can be
# added to conf/spark-env.sh to use the legacy Arrow IPC format:
ARROW_PRE_0_15_IPC_FORMAT=1
# This will instruct PyArrow >= 0.15.0 to use the legacy IPC format with the older Arrow Java that is
# in Spark 2.3.x and 2.4.x. Not setting this environment variable will lead to a similar error as
# described in SPARK-29367 when running pandas_udfs or toPandas() with Arrow enabled. More information
# about the Arrow IPC change can be read on the Arrow 0.15.0 release blog.
# REF:
# https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html#compatibiliy-setting-for-pyarrow--0150-and-spark-23x-24xJoins¶
![]("imgs/join-overview.png")
In [61]:
!ls
PySpark-by-Example.ipynb data html imgs
In [62]:
# ![title]("img/picture.png")
Download police station data
In [63]:
# !wget -O data/police-stations.csv https://data.cityofchicago.org/api/views/z8bn-74gv/rows.csv?accessType=DOWNLOAD
# !ls -l data/
The reported crimes dataset has only the district number. Add the district name by joining with the police station dataset
In [64]:
ps = spark.read.csv("data/police-stations.csv", header=True)
ps.show(5, truncate=False)
+------------+--------------+--------------------+-------+-----+-----+-------------------------------------------------------------------------------+------------+------------+------------+------------+------------+-----------+------------+-------------------------------+ |DISTRICT |DISTRICT NAME |ADDRESS |CITY |STATE|ZIP |WEBSITE |PHONE |FAX |TTY |X COORDINATE|Y COORDINATE|LATITUDE |LONGITUDE |LOCATION | +------------+--------------+--------------------+-------+-----+-----+-------------------------------------------------------------------------------+------------+------------+------------+------------+------------+-----------+------------+-------------------------------+ |Headquarters|Headquarters |3510 S Michigan Ave |Chicago|IL |60653|http://home.chicagopolice.org |null |null |null |1177731.401 |1881697.404 |41.83070169|-87.62339535|(41.8307016873, -87.6233953459)| |1 |Central |1718 S State St |Chicago|IL |60616|http://home.chicagopolice.org/community/districts/1st-district-central/ |312-745-4290|312-745-3694|312-745-3693|1176569.052 |1891771.704 |41.85837259|-87.62735617|(41.8583725929, -87.627356171) | |6 |Gresham |7808 S Halsted St |Chicago|IL |60620|http://home.chicagopolice.org/community/districts/6th-district-gresham/ |312-745-3617|312-745-3649|312-745-3639|1172283.013 |1853022.646 |41.75213684|-87.64422891|(41.7521368378, -87.6442289066)| |11 |Harrison |3151 W Harrison St |Chicago|IL |60612|http://home.chicagopolice.org/community/districts/11th-district-harrison/ |312-746-8386|312-746-4281|312-746-5151|1155244.069 |1897148.755 |41.87358229|-87.70548813|(41.8735822883, -87.705488126) | |16 |Jefferson Park|5151 N Milwaukee Ave|Chicago|IL |60630|http://home.chicagopolice.org/community/districts/16th-district-jefferson-park/|312-742-4480|312-742-4421|312-742-4423|1138480.758 |1933660.473 |41.97409445|-87.76614884|(41.9740944511, -87.7661488432)| +------------+--------------+--------------------+-------+-----+-----+-------------------------------------------------------------------------------+------------+------------+------------+------------+------------+-----------+------------+-------------------------------+ only showing top 5 rows
In [65]:
ps.select(col('DISTRICT')).distinct().show(20)
+------------+ | DISTRICT| +------------+ | 7| | 15| | 11| | 3| | 8| | 22| | 16| | 5| | 18| | 17| | 6| | 19| | 25| |Headquarters| | 24| | 9| | 1| | 20| | 10| | 4| +------------+ only showing top 20 rows
In [66]:
rc.select('District').distinct().show(20)
+--------+ |District| +--------+ | 009| | 012| | 024| | null| | 031| | 015| | 006| | 019| | 020| | 011| | 025| | 003| | 005| | 016| | 018| | 008| | 022| | 001| | 014| | 010| +--------+ only showing top 20 rows
In [67]:
from pyspark.sql.functions import lpad
In [68]:
help(lpad)
Help on function lpad in module pyspark.sql.functions:
lpad(col, len, pad)
Left-pad the string column to width `len` with `pad`.
>>> df = spark.createDataFrame([('abcd',)], ['s',])
>>> df.select(lpad(df.s, 6, '#').alias('s')).collect()
[Row(s='##abcd')]
.. versionadded:: 1.5
In [69]:
ps.select(lpad(col("DISTRICT"), 3, "0")).show()
+--------------------+ |lpad(DISTRICT, 3, 0)| +--------------------+ | Hea| | 001| | 006| | 011| | 016| | 024| | 002| | 007| | 025| | 010| | 015| | 003| | 019| | 014| | 008| | 004| | 020| | 018| | 012| | ",C| +--------------------+ only showing top 20 rows
In [70]:
ps = ps.withColumn("Format District", lpad(col("DISTRICT"), 3, "0"))
ps.show(5, truncate=False)
+------------+--------------+--------------------+-------+-----+-----+-------------------------------------------------------------------------------+------------+------------+------------+------------+------------+-----------+------------+-------------------------------+---------------+ |DISTRICT |DISTRICT NAME |ADDRESS |CITY |STATE|ZIP |WEBSITE |PHONE |FAX |TTY |X COORDINATE|Y COORDINATE|LATITUDE |LONGITUDE |LOCATION |Format District| +------------+--------------+--------------------+-------+-----+-----+-------------------------------------------------------------------------------+------------+------------+------------+------------+------------+-----------+------------+-------------------------------+---------------+ |Headquarters|Headquarters |3510 S Michigan Ave |Chicago|IL |60653|http://home.chicagopolice.org |null |null |null |1177731.401 |1881697.404 |41.83070169|-87.62339535|(41.8307016873, -87.6233953459)|Hea | |1 |Central |1718 S State St |Chicago|IL |60616|http://home.chicagopolice.org/community/districts/1st-district-central/ |312-745-4290|312-745-3694|312-745-3693|1176569.052 |1891771.704 |41.85837259|-87.62735617|(41.8583725929, -87.627356171) |001 | |6 |Gresham |7808 S Halsted St |Chicago|IL |60620|http://home.chicagopolice.org/community/districts/6th-district-gresham/ |312-745-3617|312-745-3649|312-745-3639|1172283.013 |1853022.646 |41.75213684|-87.64422891|(41.7521368378, -87.6442289066)|006 | |11 |Harrison |3151 W Harrison St |Chicago|IL |60612|http://home.chicagopolice.org/community/districts/11th-district-harrison/ |312-746-8386|312-746-4281|312-746-5151|1155244.069 |1897148.755 |41.87358229|-87.70548813|(41.8735822883, -87.705488126) |011 | |16 |Jefferson Park|5151 N Milwaukee Ave|Chicago|IL |60630|http://home.chicagopolice.org/community/districts/16th-district-jefferson-park/|312-742-4480|312-742-4421|312-742-4423|1138480.758 |1933660.473 |41.97409445|-87.76614884|(41.9740944511, -87.7661488432)|016 | +------------+--------------+--------------------+-------+-----+-----+-------------------------------------------------------------------------------+------------+------------+------------+------------+------------+-----------+------------+-------------------------------+---------------+ only showing top 5 rows
In [71]:
rc.join(ps, rc['District'] == ps["Format District"], "left_outer").show()
+--------+-----------+-------------------+--------------------+----+--------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+--------+--------------+--------------------+-------+-----+-----+--------------------+------------+------------+------------+------------+------------+-----------+------------+--------------------+---------------+ | ID|Case Number| Date| Block|IUCR| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year| Updated On|Latitude|Longitude|Location|DISTRICT| DISTRICT NAME| ADDRESS| CITY|STATE| ZIP| WEBSITE| PHONE| FAX| TTY|X COORDINATE|Y COORDINATE| LATITUDE| LONGITUDE| LOCATION|Format District| +--------+-----------+-------------------+--------------------+----+--------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+--------+--------------+--------------------+-------+-----+-----+--------------------+------------+------------+------------+------------+------------+-----------+------------+--------------------+---------------+ |11034701| JA366925|2001-01-01 11:00:00| 016XX E 86TH PL|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| RESIDENCE| false| false|0412| 004| 8| 45| 11| null| null|2001|08/05/2017 03:50:...| null| null| null| 4| South Chicago| 2255 E 103rd St|Chicago| IL|60617|http://home.chica...|312-747-7581|312-747-5276|312-747-9169| 1193131.299| 1837090.265|41.70793329|-87.56834912|(41.7079332906, -...| 004| |11227287| JB147188|2017-10-08 03:00:00| 092XX S RACINE AVE|0281| CRIM SEXUAL ASSAULT| NON-AGGRAVATED| RESIDENCE| false| false|2222| 022| 21| 73| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| 22| Morgan Park| 1900 W Monterey Ave|Chicago| IL|60643|http://home.chica...|312-745-0710|312-745-0814|312-745-0569| 1165825.476| 1830851.333|41.69143478|-87.66852039|(41.6914347795, -...| 022| |11227583| JB147595|2017-03-28 14:00:00| 026XX W 79TH ST|0620| BURGLARY| UNLAWFUL ENTRY| OTHER| false| false|0835| 008| 18| 70| 05| null| null|2017|02/11/2018 03:57:...| null| null| null| 8| Chicago Lawn| 3420 W 63rd St|Chicago| IL|60629|http://home.chica...|312-747-8730|312-747-8545|312-747-8116| 1154575.242| 1862672.049|41.77898719|-87.70886382|(41.778987189, -8...| 008| |11227293| JB147230|2017-09-09 20:17:00|060XX S EBERHART AVE|0810| THEFT| OVER $500| RESIDENCE| false| false|0313| 003| 20| 42| 06| null| null|2017|02/11/2018 03:57:...| null| null| null| 3|Grand Crossing|7040 S Cottage Gr...|Chicago| IL|60637|http://home.chica...|312-747-8201|312-747-5479|312-747-9168| 1182739.183| 1858317.732|41.76643089|-87.60574786|(41.7664308925, -...| 003| |11227634| JB147599|2017-08-26 10:00:00| 001XX W RANDOLPH ST|0281| CRIM SEXUAL ASSAULT| NON-AGGRAVATED| HOTEL/MOTEL| false| false|0122| 001| 42| 32| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| 1| Central| 1718 S State St|Chicago| IL|60616|http://home.chica...|312-745-4290|312-745-3694|312-745-3693| 1176569.052| 1891771.704|41.85837259|-87.62735617|(41.8583725929, -...| 001| |11227517| JB138481|2013-02-10 00:00:00|071XX S LAFAYETTE...|0266| CRIM SEXUAL ASSAULT| PREDATORY| RESIDENCE| false| false|0731| 007| 6| 69| 02| null| null|2013|02/11/2018 03:57:...| null| null| null| 7| Englewood| 1438 W 63rd St|Chicago| IL|60636|http://home.chica...|312-747-8223|312-747-6558|312-747-6652| 1167659.235| 1863005.522|41.77963154|-87.66088702|(41.7796315359, -...| 007| |11227503| JB146383|2015-01-01 00:01:00|061XX S KILBOURN AVE|1751|OFFENSE INVOLVING...|CRIM SEX ABUSE BY...| RESIDENCE| false| true|0813| 008| 13| 65| 17| null| null|2015|04/12/2019 04:00:...| null| null| null| 8| Chicago Lawn| 3420 W 63rd St|Chicago| IL|60629|http://home.chica...|312-747-8730|312-747-8545|312-747-8116| 1154575.242| 1862672.049|41.77898719|-87.70886382|(41.778987189, -8...| 008| |11227508| JB146365|2017-01-01 00:01:00| 027XX S WHIPPLE ST|1754|OFFENSE INVOLVING...|AGG SEX ASSLT OF ...| RESIDENCE| false| false|1033| 010| 12| 30| 02| null| null|2017|02/11/2018 03:57:...| null| null| null| 10| Ogden| 3315 W Ogden Ave|Chicago| IL|60623|http://home.chica...|312-747-7511|312-747-7429|312-747-7471| 1154500.753| 1890985.501|41.85668453|-87.70838196|(41.8566845327, -...| 010| |11022695| JA353568|2017-07-17 10:10:00| 021XX W MC LEAN AVE|0810| THEFT| OVER $500| RESIDENCE| false| false|1432| 014| 32| 22| 06| null| null|2017|07/24/2017 03:54:...| null| null| null| 14| Shakespeare|2150 N California...|Chicago| IL|60647|http://home.chica...|312-744-8250|312-744-2422|312-744-8260| 1157304.426| 1914481.521|41.92110332|-87.69745182|(41.9211033246, -...| 014| |11227633| JB147500|2017-12-28 15:55:00|011XX S MICHIGAN AVE|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| null| false| false|0123| 001| 2| 32| 11| null| null|2017|02/11/2018 03:57:...| null| null| null| 1| Central| 1718 S State St|Chicago| IL|60616|http://home.chica...|312-745-4290|312-745-3694|312-745-3693| 1176569.052| 1891771.704|41.85837259|-87.62735617|(41.8583725929, -...| 001| |11227586| JB147613|2017-02-10 12:00:00|089XX S COTTAGE G...|1310| CRIMINAL DAMAGE| TO PROPERTY| APARTMENT| false| false|0633| 006| 8| 44| 14| null| null|2017|02/11/2018 03:57:...| null| null| null| 6| Gresham| 7808 S Halsted St|Chicago| IL|60620|http://home.chica...|312-745-3617|312-745-3649|312-745-3639| 1172283.013| 1853022.646|41.75213684|-87.64422891|(41.7521368378, -...| 006| |11227475| JB147314|2017-11-22 02:42:00|056XX N CHRISTIAN...|2826| OTHER OFFENSE|HARASSMENT BY ELE...| APARTMENT| false| true|1711| 017| 39| 13| 26| null| null|2017|02/11/2018 03:57:...| null| null| null| 17| Albany Park| 4650 N Pulaski Rd|Chicago| IL|60630|http://home.chica...|312-742-4410|312-742-5411|312-742-5451| 1148843.91| 1930801.058|41.96605342|-87.72811456|(41.9660534171, -...| 017| |11227247| JB147078|2012-01-01 09:00:00|105XX S INDIANAPO...|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| RESIDENCE| false| false|0432| 004| 10| 52| 11| null| null|2012|02/11/2018 03:57:...| null| null| null| 4| South Chicago| 2255 E 103rd St|Chicago| IL|60617|http://home.chica...|312-747-7581|312-747-5276|312-747-9169| 1193131.299| 1837090.265|41.70793329|-87.56834912|(41.7079332906, -...| 004| |11036284| JA370282|2017-07-29 15:40:00| 014XX W Devon Ave|0820| THEFT| $500 AND UNDER| SIDEWALK| false| false|2432| 024| 40| 1| 06| null| null|2017|08/05/2017 03:50:...| null| null| null| 24| Rogers Park| 6464 N Clark St|Chicago| IL|60626|http://home.chica...|312-744-5907|312-744-6928|312-744-7603| 1164193.588| 1943199.401|41.99976348|-87.67132429|(41.9997634842, -...| 024| |11227509| JB146413|2017-01-22 00:01:00|079XX S JEFFERY BLVD|1752|OFFENSE INVOLVING...|AGG CRIM SEX ABUS...| APARTMENT| false| false|0414| 004| 8| 46| 20| null| null|2017|02/11/2018 03:57:...| null| null| null| 4| South Chicago| 2255 E 103rd St|Chicago| IL|60617|http://home.chica...|312-747-7581|312-747-5276|312-747-9169| 1193131.299| 1837090.265|41.70793329|-87.56834912|(41.7079332906, -...| 004| |11227407| JB147329|2017-10-14 00:01:00|037XX N SOUTHPORT...|1150| DECEPTIVE PRACTICE| CREDIT CARD FRAUD| OTHER| false| false|1922| 019| 44| 6| 11| null| null|2017|02/22/2018 03:56:...| null| null| null| 19| Town Hall| 850 W Addison St|Chicago| IL|60613|http://home.chica...|312-744-8320|312-744-4481|312-744-8011| 1169730.744| 1924160.317|41.94740046|-87.65151202|(41.9474004564, -...| 019| |11028056| JA359834|2014-10-15 15:00:00| 047XX S PULASKI RD|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...|PARKING LOT/GARAG...| false| false|0821| 008| 14| 57| 11| null| null|2014|07/24/2017 03:54:...| null| null| null| 8| Chicago Lawn| 3420 W 63rd St|Chicago| IL|60629|http://home.chica...|312-747-8730|312-747-8545|312-747-8116| 1154575.242| 1862672.049|41.77898719|-87.70886382|(41.778987189, -8...| 008| |11028299| JA360073|2015-05-30 00:00:00| 074XX S HARVARD AVE|1753|OFFENSE INVOLVING...|SEX ASSLT OF CHIL...| RESIDENCE| false| true|0731| 007| 17| 69| 02| null| null|2015|05/15/2018 03:52:...| null| null| null| 7| Englewood| 1438 W 63rd St|Chicago| IL|60636|http://home.chica...|312-747-8223|312-747-6558|312-747-6652| 1167659.235| 1863005.522|41.77963154|-87.66088702|(41.7796315359, -...| 007| |11028290| JA360134|2015-05-30 00:00:00| 074XX S HARVARD AVE|1753|OFFENSE INVOLVING...|SEX ASSLT OF CHIL...| RESIDENCE| false| true|0731| 007| 17| 69| 02| null| null|2015|05/22/2018 04:05:...| null| null| null| 7| Englewood| 1438 W 63rd St|Chicago| IL|60636|http://home.chica...|312-747-8223|312-747-6558|312-747-6652| 1167659.235| 1863005.522|41.77963154|-87.66088702|(41.7796315359, -...| 007| |11041422| JA375398|2017-07-29 10:00:00|022XX W WINNEMAC AVE|0810| THEFT| OVER $500| RESIDENCE-GARAGE| false| false|2031| 020| 47| 4| 06| null| null|2017|08/05/2017 03:50:...| null| null| null| 20| Lincoln| 5400 N Lincoln Ave|Chicago| IL|60625|http://home.chica...|312-742-8714|312-742-8803|312-742-8841| 1158399.146| 1935788.826|41.97954951|-87.69284451|(41.9795495131, -...| 020| +--------+-----------+-------------------+--------------------+----+--------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+--------------------+--------+---------+--------+--------+--------------+--------------------+-------+-----+-----+--------------------+------------+------------+------------+------------+------------+-----------+------------+--------------------+---------------+ only showing top 20 rows
In [72]:
ps.columns
Out[72]:
['DISTRICT', 'DISTRICT NAME', 'ADDRESS', 'CITY', 'STATE', 'ZIP', 'WEBSITE', 'PHONE', 'FAX', 'TTY', 'X COORDINATE', 'Y COORDINATE', 'LATITUDE', 'LONGITUDE', 'LOCATION', 'Format District']
In [73]:
rc.join(ps, rc['District'] == ps["Format District"], "left_outer").drop(
'ADDRESS',
'CITY',
'STATE',
'ZIP',
'WEBSITE',
'PHONE',
'FAX',
'TTY',
'X COORDINATE',
'Y COORDINATE',
'LATITUDE',
'LONGITUDE',
'LOCATION'
).show()
+--------+-----------+-------------------+--------------------+----+--------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+----+--------------------+--------+--------------+---------------+ | ID|Case Number| Date| Block|IUCR| Primary Type| Description|Location Description|Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|Year| Updated On|DISTRICT| DISTRICT NAME|Format District| +--------+-----------+-------------------+--------------------+----+--------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+----+--------------------+--------+--------------+---------------+ |11034701| JA366925|2001-01-01 11:00:00| 016XX E 86TH PL|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| RESIDENCE| false| false|0412| 004| 8| 45| 11|2001|08/05/2017 03:50:...| 4| South Chicago| 004| |11227287| JB147188|2017-10-08 03:00:00| 092XX S RACINE AVE|0281| CRIM SEXUAL ASSAULT| NON-AGGRAVATED| RESIDENCE| false| false|2222| 022| 21| 73| 02|2017|02/11/2018 03:57:...| 22| Morgan Park| 022| |11227583| JB147595|2017-03-28 14:00:00| 026XX W 79TH ST|0620| BURGLARY| UNLAWFUL ENTRY| OTHER| false| false|0835| 008| 18| 70| 05|2017|02/11/2018 03:57:...| 8| Chicago Lawn| 008| |11227293| JB147230|2017-09-09 20:17:00|060XX S EBERHART AVE|0810| THEFT| OVER $500| RESIDENCE| false| false|0313| 003| 20| 42| 06|2017|02/11/2018 03:57:...| 3|Grand Crossing| 003| |11227634| JB147599|2017-08-26 10:00:00| 001XX W RANDOLPH ST|0281| CRIM SEXUAL ASSAULT| NON-AGGRAVATED| HOTEL/MOTEL| false| false|0122| 001| 42| 32| 02|2017|02/11/2018 03:57:...| 1| Central| 001| |11227517| JB138481|2013-02-10 00:00:00|071XX S LAFAYETTE...|0266| CRIM SEXUAL ASSAULT| PREDATORY| RESIDENCE| false| false|0731| 007| 6| 69| 02|2013|02/11/2018 03:57:...| 7| Englewood| 007| |11227503| JB146383|2015-01-01 00:01:00|061XX S KILBOURN AVE|1751|OFFENSE INVOLVING...|CRIM SEX ABUSE BY...| RESIDENCE| false| true|0813| 008| 13| 65| 17|2015|04/12/2019 04:00:...| 8| Chicago Lawn| 008| |11227508| JB146365|2017-01-01 00:01:00| 027XX S WHIPPLE ST|1754|OFFENSE INVOLVING...|AGG SEX ASSLT OF ...| RESIDENCE| false| false|1033| 010| 12| 30| 02|2017|02/11/2018 03:57:...| 10| Ogden| 010| |11022695| JA353568|2017-07-17 10:10:00| 021XX W MC LEAN AVE|0810| THEFT| OVER $500| RESIDENCE| false| false|1432| 014| 32| 22| 06|2017|07/24/2017 03:54:...| 14| Shakespeare| 014| |11227633| JB147500|2017-12-28 15:55:00|011XX S MICHIGAN AVE|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| null| false| false|0123| 001| 2| 32| 11|2017|02/11/2018 03:57:...| 1| Central| 001| |11227586| JB147613|2017-02-10 12:00:00|089XX S COTTAGE G...|1310| CRIMINAL DAMAGE| TO PROPERTY| APARTMENT| false| false|0633| 006| 8| 44| 14|2017|02/11/2018 03:57:...| 6| Gresham| 006| |11227475| JB147314|2017-11-22 02:42:00|056XX N CHRISTIAN...|2826| OTHER OFFENSE|HARASSMENT BY ELE...| APARTMENT| false| true|1711| 017| 39| 13| 26|2017|02/11/2018 03:57:...| 17| Albany Park| 017| |11227247| JB147078|2012-01-01 09:00:00|105XX S INDIANAPO...|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...| RESIDENCE| false| false|0432| 004| 10| 52| 11|2012|02/11/2018 03:57:...| 4| South Chicago| 004| |11036284| JA370282|2017-07-29 15:40:00| 014XX W Devon Ave|0820| THEFT| $500 AND UNDER| SIDEWALK| false| false|2432| 024| 40| 1| 06|2017|08/05/2017 03:50:...| 24| Rogers Park| 024| |11227509| JB146413|2017-01-22 00:01:00|079XX S JEFFERY BLVD|1752|OFFENSE INVOLVING...|AGG CRIM SEX ABUS...| APARTMENT| false| false|0414| 004| 8| 46| 20|2017|02/11/2018 03:57:...| 4| South Chicago| 004| |11227407| JB147329|2017-10-14 00:01:00|037XX N SOUTHPORT...|1150| DECEPTIVE PRACTICE| CREDIT CARD FRAUD| OTHER| false| false|1922| 019| 44| 6| 11|2017|02/22/2018 03:56:...| 19| Town Hall| 019| |11028056| JA359834|2014-10-15 15:00:00| 047XX S PULASKI RD|1153| DECEPTIVE PRACTICE|FINANCIAL IDENTIT...|PARKING LOT/GARAG...| false| false|0821| 008| 14| 57| 11|2014|07/24/2017 03:54:...| 8| Chicago Lawn| 008| |11028299| JA360073|2015-05-30 00:00:00| 074XX S HARVARD AVE|1753|OFFENSE INVOLVING...|SEX ASSLT OF CHIL...| RESIDENCE| false| true|0731| 007| 17| 69| 02|2015|05/15/2018 03:52:...| 7| Englewood| 007| |11028290| JA360134|2015-05-30 00:00:00| 074XX S HARVARD AVE|1753|OFFENSE INVOLVING...|SEX ASSLT OF CHIL...| RESIDENCE| false| true|0731| 007| 17| 69| 02|2015|05/22/2018 04:05:...| 7| Englewood| 007| |11041422| JA375398|2017-07-29 10:00:00|022XX W WINNEMAC AVE|0810| THEFT| OVER $500| RESIDENCE-GARAGE| false| false|2031| 020| 47| 4| 06|2017|08/05/2017 03:50:...| 20| Lincoln| 020| +--------+-----------+-------------------+--------------------+----+--------------------+--------------------+--------------------+------+--------+----+--------+----+--------------+--------+----+--------------------+--------+--------------+---------------+ only showing top 20 rows
Challenge questions¶
What is the most frequently reported non-criminal activity?
In [74]:
rc.select('Primary Type').distinct().count()
Out[74]:
36
In [75]:
rc.select('Primary Type').distinct().orderBy(col('Primary Type')).show(35, truncate=False)
+---------------------------------+ |Primary Type | +---------------------------------+ |ARSON | |ASSAULT | |BATTERY | |BURGLARY | |CONCEALED CARRY LICENSE VIOLATION| |CRIM SEXUAL ASSAULT | |CRIMINAL DAMAGE | |CRIMINAL SEXUAL ASSAULT | |CRIMINAL TRESPASS | |DECEPTIVE PRACTICE | |DOMESTIC VIOLENCE | |GAMBLING | |HOMICIDE | |HUMAN TRAFFICKING | |INTERFERENCE WITH PUBLIC OFFICER | |INTIMIDATION | |KIDNAPPING | |LIQUOR LAW VIOLATION | |MOTOR VEHICLE THEFT | |NARCOTICS | |NON - CRIMINAL | |NON-CRIMINAL | |NON-CRIMINAL (SUBJECT SPECIFIED) | |OBSCENITY | |OFFENSE INVOLVING CHILDREN | |OTHER NARCOTIC VIOLATION | |OTHER OFFENSE | |PROSTITUTION | |PUBLIC INDECENCY | |PUBLIC PEACE VIOLATION | |RITUALISM | |ROBBERY | |SEX OFFENSE | |STALKING | |THEFT | +---------------------------------+ only showing top 35 rows
In [76]:
nc = rc.filter( (col('Primary Type') == 'NON - CRIMINAL') | (col('Primary Type') == 'NON-CRIMINAL ') | (col('Primary Type') == 'NON-CRIMINAL (SUBJECT SPECIFIED)') )
nc.show(truncate=False)
+--------+-----------+-------------------+----------------------+----+--------------------------------+--------------------------------------+-------------------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------------------+------------+-------------+-----------------------------+ |ID |Case Number|Date |Block |IUCR|Primary Type |Description |Location Description |Arrest|Domestic|Beat|District|Ward|Community Area|FBI Code|X Coordinate|Y Coordinate|Year|Updated On |Latitude |Longitude |Location | +--------+-----------+-------------------+----------------------+----+--------------------------------+--------------------------------------+-------------------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------------------+------------+-------------+-----------------------------+ |10062441|HY250685 |2015-05-07 13:20:00|012XX S HARDING AVE |5114|NON - CRIMINAL |FOID - REVOCATION |RESIDENCE |false |false |1011|010 |24 |29 |26 |1150243 |1894129 |2015|02/10/2018 03:50:01 PM|41.865394646|-87.723928428|(41.865394646, -87.723928428)| |10064717|HY253344 |2015-05-08 13:15:00|051XX S WENTWORTH AVE |5114|NON - CRIMINAL |FOID - REVOCATION |POLICE FACILITY/VEH PARKING LOT|false |false |0225|002 |3 |37 |26 |1175826 |1871120 |2015|02/10/2018 03:50:01 PM|41.80171934 |-87.630703621|(41.80171934, -87.630703621) | |10072565|HY261001 |2015-05-14 10:30:00|006XX N WELLS ST |5114|NON - CRIMINAL |FOID - REVOCATION |STREET |false |false |1832|018 |42 |8 |26 |1174623 |1904537 |2015|02/10/2018 03:50:01 PM|41.89344506 |-87.634117632|(41.89344506, -87.634117632) | |10109156|HY297801 |2015-06-12 09:00:00|053XX S NEVA AVE |5114|NON - CRIMINAL |FOID - REVOCATION |RESIDENCE |false |false |0811|008 |23 |56 |26 |1129584 |1868411 |2015|02/10/2018 03:50:01 PM|41.795197456|-87.800355525|(41.795197456, -87.800355525)| |10115077|HY304017 |2015-06-16 19:00:00|081XX S WHIPPLE ST |5114|NON - CRIMINAL |FOID - REVOCATION |RESIDENCE |false |false |0835|008 |18 |70 |26 |1157460 |1850515 |2015|02/10/2018 03:50:01 PM|41.745568408|-87.698616805|(41.745568408, -87.698616805)| |10124614|HY313353 |2015-06-23 17:05:00|085XX W WINNEMAC AVE |5114|NON - CRIMINAL |FOID - REVOCATION |RESIDENCE |true |false |1614|016 |41 |76 |26 |1118538 |1932386 |2015|02/10/2018 03:50:01 PM|41.970936043|-87.839512637|(41.970936043, -87.839512637)| |10125981|HY314305 |2015-06-24 15:30:00|021XX N CALIFORNIA AVE|5114|NON - CRIMINAL |FOID - REVOCATION |POLICE FACILITY/VEH PARKING LOT|false |false |1414|014 |35 |22 |26 |1157345 |1914452 |2015|02/10/2018 03:50:01 PM|41.921021491|-87.69730355 |(41.921021491, -87.69730355) | |10127418|HY315600 |2015-06-25 15:30:00|022XX W 51ST ST |5114|NON - CRIMINAL |FOID - REVOCATION |RESIDENCE |false |false |0931|009 |16 |63 |26 |1162194 |1870807 |2015|02/10/2018 03:50:01 PM|41.801155339|-87.68070621 |(41.801155339, -87.68070621) | |10127785|HY316123 |2015-06-25 22:45:00|051XX N MILWAUKEE AVE |5114|NON - CRIMINAL |FOID - REVOCATION |POLICE FACILITY/VEH PARKING LOT|true |false |1623|016 |45 |11 |26 |1138424 |1933673 |2015|02/10/2018 03:50:01 PM|41.974129858|-87.766357256|(41.974129858, -87.766357256)| |10128364|HY316525 |2015-06-26 10:45:00|035XX S MICHIGAN AVE |5114|NON - CRIMINAL |FOID - REVOCATION |POLICE FACILITY/VEH PARKING LOT|false |false |0213|002 |3 |35 |26 |1177772 |1881665 |2015|02/10/2018 03:50:01 PM|41.830611847|-87.623247369|(41.830611847, -87.623247369)| |10181019|HY369010 |2015-08-01 10:15:00|079XX S KEDZIE AVE |5114|NON - CRIMINAL |FOID - REVOCATION |RESIDENCE |false |false |0835|008 |18 |70 |26 |1156427 |1851731 |2015|02/10/2018 03:50:01 PM|41.748926157|-87.70236927 |(41.748926157, -87.70236927) | |10202049|HY389275 |2015-08-19 18:38:00|025XX W MARQUETTE RD |5114|NON - CRIMINAL |FOID - REVOCATION |APARTMENT |false |false |0832|008 |15 |66 |26 |1160388 |1860136 |2015|02/10/2018 03:50:01 PM|41.771910102|-87.687623385|(41.771910102, -87.687623385)| |10221247|HY406742 |2015-09-01 22:00:00|031XX W HARRISON ST |5114|NON - CRIMINAL |FOID - REVOCATION |POLICE FACILITY/VEH PARKING LOT|false |false |1134|011 |24 |27 |26 |1155457 |1897193 |2015|02/10/2018 03:50:01 PM|41.873699424|-87.704705156|(41.873699424, -87.704705156)| |10233370|HY421530 |2015-09-13 10:45:00|010XX S MENARD AVE |5114|NON - CRIMINAL |FOID - REVOCATION |RESIDENCE |true |false |1513|015 |29 |25 |26 |1137869 |1895239 |2015|02/10/2018 03:50:01 PM|41.868672696|-87.769327726|(41.868672696, -87.769327726)| |10241515|HY429346 |2015-09-19 07:45:00|051XX N MILWAUKEE AVE |5114|NON - CRIMINAL |FOID - REVOCATION |POLICE FACILITY/VEH PARKING LOT|true |false |1623|016 |45 |11 |26 |1138424 |1933673 |2015|02/10/2018 03:50:01 PM|41.974129858|-87.766357256|(41.974129858, -87.766357256)| |10280097|HY468448 |2015-10-19 18:50:00|033XX W OGDEN AVE |5114|NON - CRIMINAL |FOID - REVOCATION |POLICE FACILITY/VEH PARKING LOT|false |false |1024|010 |24 |29 |26 |1154489 |1891024 |2015|02/10/2018 03:50:01 PM|41.856790413|-87.708424071|(41.856790413, -87.708424071)| |10300617|HY489191 |2015-11-03 15:00:00|031XX S HALSTED ST |5114|NON - CRIMINAL |FOID - REVOCATION |RESIDENCE |false |false |0913|009 |11 |60 |26 |1171479 |1884120 |2015|02/10/2018 03:50:01 PM|41.837489008|-87.646264459|(41.837489008, -87.646264459)| |10344075|HY535016 |2015-12-12 20:50:00|025XX W 80TH PL |5114|NON - CRIMINAL |FOID - REVOCATION |VEHICLE NON-COMMERCIAL |false |false |0835|008 |18 |70 |26 |1160683 |1851174 |2015|02/10/2018 03:50:01 PM|41.747310961|-87.68678891 |(41.747310961, -87.68678891) | |10371674|HZ107512 |2016-01-07 17:36:00|051XX S WENTWORTH AVE |5073|NON-CRIMINAL (SUBJECT SPECIFIED)|NOTIFICATION OF CIVIL NO CONTACT ORDER|JAIL / LOCK-UP FACILITY |false |true |0225|002 |3 |37 |26 |1175826 |1871120 |2016|02/10/2018 03:50:01 PM|41.80171934 |-87.630703621|(41.80171934, -87.630703621) | |10397129|HZ133234 |2016-01-29 15:30:00|006XX W HARRISON ST |5114|NON - CRIMINAL |FOID - REVOCATION |STREET |false |false |0124|001 |2 |28 |26 |1172257 |1897564 |2016|02/10/2018 03:50:01 PM|41.874363279|-87.643013039|(41.874363279, -87.643013039)| +--------+-----------+-------------------+----------------------+----+--------------------------------+--------------------------------------+-------------------------------+------+--------+----+--------+----+--------------+--------+------------+------------+----+----------------------+------------+-------------+-----------------------------+ only showing top 20 rows
In [77]:
nc.groupBy(col('Description')).count().orderBy('count', ascending=False).show(truncate=False)
+--------------------------------------+-----+ |Description |count| +--------------------------------------+-----+ |FOID - REVOCATION |38 | |NOTIFICATION OF CIVIL NO CONTACT ORDER|9 | +--------------------------------------+-----+
Using a bar chart, plot which day of the week has the most number of reported crime.
In [78]:
from pyspark.sql.functions import dayofweek
In [79]:
help(dayofweek)
Help on function dayofweek in module pyspark.sql.functions:
dayofweek(col)
Extract the day of the week of a given date as integer.
>>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
>>> df.select(dayofweek('dt').alias('day')).collect()
[Row(day=4)]
.. versionadded:: 2.3
In [80]:
rc.select(col('Date'), dayofweek(col('Date'))).show(5)
+-------------------+---------------+ | Date|dayofweek(Date)| +-------------------+---------------+ |2001-01-01 11:00:00| 2| |2017-10-08 03:00:00| 1| |2017-03-28 14:00:00| 3| |2017-09-09 20:17:00| 7| |2017-08-26 10:00:00| 7| +-------------------+---------------+ only showing top 5 rows
In [81]:
from pyspark.sql.functions import date_format
In [82]:
help(date_format)
Help on function date_format in module pyspark.sql.functions:
date_format(date, format)
Converts a date/timestamp/string to a value of string in the format specified by the date
format given by the second argument.
A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
pattern letters of the Java class `java.text.SimpleDateFormat` can be used.
.. note:: Use when ever possible specialized functions like `year`. These benefit from a
specialized implementation.
>>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
>>> df.select(date_format('dt', 'MM/dd/yyy').alias('date')).collect()
[Row(date='04/08/2015')]
.. versionadded:: 1.5
In [83]:
rc.select(col('Date'), dayofweek(col('Date')), date_format(col('Date'), 'E')).orderBy("dayofweek(Date)").show(5)
+-------------------+---------------+--------------------+ | Date|dayofweek(Date)|date_format(Date, E)| +-------------------+---------------+--------------------+ |2012-01-01 09:00:00| 1| Sun| |2017-10-01 00:00:00| 1| Sun| |2017-01-22 00:01:00| 1| Sun| |2017-10-08 03:00:00| 1| Sun| |2017-01-01 00:01:00| 1| Sun| +-------------------+---------------+--------------------+ only showing top 5 rows
In [84]:
from pyspark.sql.functions import count, avg
In [85]:
ss = rc.groupBy(dayofweek(col('Date')), date_format(col('Date'), 'E')).agg(count("*")).show()
+---------------+--------------------+--------+ |dayofweek(Date)|date_format(Date, E)|count(1)| +---------------+--------------------+--------+ | 2| Mon| 952646| | 6| Fri| 1016882| | 1| Sun| 911174| | 5| Thu| 964457| | 4| Wed| 973801| | 3| Tue| 967965| | 7| Sat| 965095| +---------------+--------------------+--------+
In [86]:
ss = rc.groupBy(dayofweek(col('Date')), date_format(col('Date'), 'E')).count().orderBy('dayofweek(Date)')
In [87]:
ss.show()
+---------------+--------------------+-------+ |dayofweek(Date)|date_format(Date, E)| count| +---------------+--------------------+-------+ | 1| Sun| 911174| | 2| Mon| 952646| | 3| Tue| 967965| | 4| Wed| 973801| | 5| Thu| 964457| | 6| Fri|1016882| | 7| Sat| 965095| +---------------+--------------------+-------+
In [88]:
type(ss)
Out[88]:
pyspark.sql.dataframe.DataFrame
In [89]:
sbar = ss.withColumn("Day of Week", col("date_format(Date, E)"))
In [90]:
import plotly.express as px
fig = px.bar(sbar.toPandas(), x="Day of Week", y="count")
fig.show()
In [91]:
import importlib.util
spec = importlib.util.spec_from_file_location("plotly", "/Users/tallamjr/github/forks/plotly.py/packages/python/plotly/plotly/io/_html.py")
foo = importlib.util.module_from_spec(spec)
spec.loader.exec_module(foo)
foo.to_html(fig, include_plotlyjs=False, full_html=False)
Out[91]:
'<div> <div id="7dc7e1f5-3923-4a6d-ba62-91cd9ad9579d" class="plotly-graph-div" style="height:100%; width:100%;"> </div> <script type="text/javascript"> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("7dc7e1f5-3923-4a6d-ba62-91cd9ad9579d")) { Plotly.newPlot( "7dc7e1f5-3923-4a6d-ba62-91cd9ad9579d", [{"alignmentgroup": "True", "hovertemplate": "Day of Week=%{x}<br>count=%{y}<extra></extra>", "legendgroup": "", "marker": {"color": "#636efa"}, "name": "", "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", "type": "bar", "x": ["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"], "xaxis": "x", "y": [911174, 952646, 967965, 973801, 964457, 1016882, 965095], "yaxis": "y"}], {"barmode": "relative", "legend": {"tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": 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In [92]:
dow = [ x[0] for x in rc.groupBy(date_format(col('Date'), 'E')).count().collect() ]
dow
Out[92]:
['Sun', 'Mon', 'Thu', 'Sat', 'Wed', 'Tue', 'Fri']
In [93]:
cnt = [ x[1] for x in rc.groupBy(date_format(col('Date'), 'E')).count().collect() ]
cnt
Out[93]:
[911174, 952646, 964457, 965095, 973801, 967965, 1016882]
In [94]:
import pandas as pd
In [95]:
data = {'Day of Week': dow, 'Count': cnt}
data
Out[95]:
{'Count': [911174, 952646, 964457, 965095, 973801, 967965, 1016882],
'Day of Week': ['Sun', 'Mon', 'Thu', 'Sat', 'Wed', 'Tue', 'Fri']}
In [96]:
pdf = pd.DataFrame(data)
In [97]:
sdf = spark.createDataFrame(pdf)
sdf.show()
+-----------+-------+ |Day of Week| Count| +-----------+-------+ | Sun| 911174| | Mon| 952646| | Thu| 964457| | Sat| 965095| | Wed| 973801| | Tue| 967965| | Fri|1016882| +-----------+-------+
/usr/local/Cellar/apache-spark/2.4.5/libexec/python/pyspark/sql/session.py:714: UserWarning: createDataFrame attempted Arrow optimization because 'spark.sql.execution.arrow.enabled' is set to true; however, failed by the reason below: An error occurred while calling z:org.apache.spark.sql.api.python.PythonSQLUtils.readArrowStreamFromFile. : java.lang.IllegalArgumentException at java.nio.ByteBuffer.allocate(ByteBuffer.java:334) at org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543) at org.apache.spark.sql.execution.arrow.ArrowConverters$$anon$3.readNextBatch(ArrowConverters.scala:243) at org.apache.spark.sql.execution.arrow.ArrowConverters$$anon$3.<init>(ArrowConverters.scala:229) at org.apache.spark.sql.execution.arrow.ArrowConverters$.getBatchesFromStream(ArrowConverters.scala:228) at org.apache.spark.sql.execution.arrow.ArrowConverters$$anonfun$readArrowStreamFromFile$2.apply(ArrowConverters.scala:216) at org.apache.spark.sql.execution.arrow.ArrowConverters$$anonfun$readArrowStreamFromFile$2.apply(ArrowConverters.scala:214) at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2543) at org.apache.spark.sql.execution.arrow.ArrowConverters$.readArrowStreamFromFile(ArrowConverters.scala:214) at org.apache.spark.sql.api.python.PythonSQLUtils$.readArrowStreamFromFile(PythonSQLUtils.scala:46) at org.apache.spark.sql.api.python.PythonSQLUtils.readArrowStreamFromFile(PythonSQLUtils.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748) Attempting non-optimization as 'spark.sql.execution.arrow.fallback.enabled' is set to true.
In [98]:
pdf
Out[98]:
| Day of Week | Count | |
|---|---|---|
| 0 | Sun | 911174 |
| 1 | Mon | 952646 |
| 2 | Thu | 964457 |
| 3 | Sat | 965095 |
| 4 | Wed | 973801 |
| 5 | Tue | 967965 |
| 6 | Fri | 1016882 |
In [99]:
import matplotlib.pyplot as plt
%matplotlib inline
In [100]:
pdf.sort_values('Count', ascending=False).plot(kind='bar', color='blue', x='Day of Week', y='Count')
plt.xlabel('Day of Week')
plt.ylabel('Number of reported crimes')
plt.title('Number of reported crimes for each of the week')
Out[100]:
Text(0.5,1,'Number of reported crimes for each of the week')
In [101]:
dwnum = rc.select(col('Date'), \
dayofweek(col('Date')), \
date_format(col('Date'), 'E')) \
.groupBy(dayofweek(col('Date'))).count().orderBy("dayofweek(Date)").show()
+---------------+-------+ |dayofweek(Date)| count| +---------------+-------+ | 1| 911174| | 2| 952646| | 3| 967965| | 4| 973801| | 5| 964457| | 6|1016882| | 7| 965095| +---------------+-------+
In [102]:
help(dayofweek)
Help on function dayofweek in module pyspark.sql.functions:
dayofweek(col)
Extract the day of the week of a given date as integer.
>>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
>>> df.select(dayofweek('dt').alias('day')).collect()
[Row(day=4)]
.. versionadded:: 2.3
In [103]:
rc.groupBy(date_format(col('Date'), 'E')).count().collect()
Out[103]:
[Row(date_format(Date, E)='Sun', count=911174), Row(date_format(Date, E)='Mon', count=952646), Row(date_format(Date, E)='Thu', count=964457), Row(date_format(Date, E)='Sat', count=965095), Row(date_format(Date, E)='Wed', count=973801), Row(date_format(Date, E)='Tue', count=967965), Row(date_format(Date, E)='Fri', count=1016882)]
Maps¶
In [104]:
ps.select("Latitude", 'Longitude').show(5)
+-----------+------------+ | Latitude| Longitude| +-----------+------------+ |41.83070169|-87.62339535| |41.85837259|-87.62735617| |41.75213684|-87.64422891| |41.87358229|-87.70548813| |41.97409445|-87.76614884| +-----------+------------+ only showing top 5 rows
In [105]:
latlong = ps.groupBy("Latitude", "Longitude").count().show(23)
+-----------+------------+-----+ | Latitude| Longitude|count| +-----------+------------+-----+ |41.85668453|-87.70838196| 1| |41.77963154|-87.66088702| 1| |41.99976348|-87.67132429| 1| |41.83739443|-87.64640771| 1| |41.96605342|-87.72811456| 1| |41.97409445|-87.76614884| 1| | null| null| 2| |41.88008346|-87.76819989| 1| |41.97954951|-87.69284451| 1| |41.69143478|-87.66852039| 1| |41.77898719|-87.70886382| 1| |41.76643089|-87.60574786| 1| |41.80181109|-87.63056018| 1| |41.92110332|-87.69745182| 1| |41.70793329|-87.56834912| 1| |41.90324165|-87.64335214| 1| |41.94740046|-87.65151202| 1| |41.85837259|-87.62735617| 1| |41.69272336|-87.60450587| 1| |41.87358229|-87.70548813| 1| |41.83070169|-87.62339535| 1| |41.75213684|-87.64422891| 1| |41.91860889|-87.76557448| 1| +-----------+------------+-----+
In [106]:
latlongrc = rc.groupBy("Latitude", "Longitude").count().orderBy('count', ascending=False)
latlongrc.show()
+------------+-------------+-----+ | Latitude| Longitude|count| +------------+-------------+-----+ | null| null|65476| |41.976290414|-87.905227221|13113| |41.754592961|-87.741528537| 9432| |41.883500187|-87.627876698| 6933| |41.897895128|-87.624096605| 4287| |41.896888586|-87.628203192| 3120| |41.909664252|-87.742728815| 3037| |41.885487535|-87.726422045| 2680| |41.904192368|-87.647000785| 2545| |41.788987036| -87.74147999| 2455| | 41.88233367|-87.627841791| 2438| |41.721627204|-87.624485177| 2380| |41.736259984|-87.628068782| 2370| |41.737094305|-87.572998178| 2284| | 41.68995741|-87.637460623| 2257| |41.739265865|-87.604893749| 2187| |41.891990384|-87.611461502| 2179| |41.868180939|-87.709271389| 2169| |41.979006297|-87.906463155| 2137| |41.736148121|-87.629070243| 2050| +------------+-------------+-----+ only showing top 20 rows
In [107]:
latlongrc = latlongrc.where(col("Latitude").isNotNull())
latlongrc.show()
+------------+-------------+-----+ | Latitude| Longitude|count| +------------+-------------+-----+ |41.976290414|-87.905227221|13113| |41.754592961|-87.741528537| 9432| |41.883500187|-87.627876698| 6933| |41.897895128|-87.624096605| 4287| |41.896888586|-87.628203192| 3120| |41.909664252|-87.742728815| 3037| |41.885487535|-87.726422045| 2680| |41.904192368|-87.647000785| 2545| |41.788987036| -87.74147999| 2455| | 41.88233367|-87.627841791| 2438| |41.721627204|-87.624485177| 2380| |41.736259984|-87.628068782| 2370| |41.737094305|-87.572998178| 2284| | 41.68995741|-87.637460623| 2257| |41.739265865|-87.604893749| 2187| |41.891990384|-87.611461502| 2179| |41.868180939|-87.709271389| 2169| |41.979006297|-87.906463155| 2137| |41.736148121|-87.629070243| 2050| |41.706070186|-87.653645803| 2004| +------------+-------------+-----+ only showing top 20 rows
In [108]:
# import plotly.express as px
# fig = px.density_mapbox(latlongrc.toPandas(), lat='Latitude', lon='Longitude', z='count', radius=10,
# center=dict(lat=0, lon=180), zoom=0,
# mapbox_style="stamen-terrain")
# fig.show()
In [109]:
latlongrc.count()
Out[109]:
860250
In [110]:
latlongrcSample = latlongrc.limit(100)
latlongrcSample.show()
+------------+-------------+-----+ | Latitude| Longitude|count| +------------+-------------+-----+ |41.976290414|-87.905227221|13113| |41.754592961|-87.741528537| 9432| |41.883500187|-87.627876698| 6933| |41.897895128|-87.624096605| 4287| |41.896888586|-87.628203192| 3120| |41.909664252|-87.742728815| 3037| |41.885487535|-87.726422045| 2680| |41.904192368|-87.647000785| 2545| |41.788987036| -87.74147999| 2455| | 41.88233367|-87.627841791| 2438| |41.721627204|-87.624485177| 2380| |41.736259984|-87.628068782| 2370| |41.737094305|-87.572998178| 2284| | 41.68995741|-87.637460623| 2257| |41.739265865|-87.604893749| 2187| |41.891990384|-87.611461502| 2179| |41.868180939|-87.709271389| 2169| |41.979006297|-87.906463155| 2137| |41.736148121|-87.629070243| 2050| |41.706070186|-87.653645803| 2004| +------------+-------------+-----+ only showing top 20 rows
In [111]:
latlongrcSample.count()
Out[111]:
100
In [112]:
latlongrcSample.printSchema()
root |-- Latitude: string (nullable = true) |-- Longitude: string (nullable = true) |-- count: long (nullable = false)
In [113]:
from pyspark.sql.types import *
In [114]:
latlongrcSampleCast = latlongrcSample.withColumn("Longitude", latlongrcSample["Longitude"].cast(DoubleType()))
In [115]:
latlongrcSampleCast = latlongrcSampleCast.withColumn("Latitude", latlongrcSample["Latitude"].cast(DoubleType()))
In [116]:
latlongrcSampleCast.printSchema()
root |-- Latitude: double (nullable = true) |-- Longitude: double (nullable = true) |-- count: long (nullable = false)
In [117]:
latlongrcSampleCast.show(2)
+------------+-------------+-----+ | Latitude| Longitude|count| +------------+-------------+-----+ |41.976290414|-87.905227221|13113| |41.754592961|-87.741528537| 9432| +------------+-------------+-----+ only showing top 2 rows
In [118]:
import plotly.express as px
fig = px.scatter_mapbox(latlongrcSampleCast.toPandas(), lat="Latitude", lon="Longitude", hover_name="count", hover_data=["count"],
color_discrete_sequence=["fuchsia"], zoom=10, height=600)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
In [119]:
import importlib.util
spec = importlib.util.spec_from_file_location("plotly", "/Users/tallamjr/github/forks/plotly.py/packages/python/plotly/plotly/io/_html.py")
foo = importlib.util.module_from_spec(spec)
spec.loader.exec_module(foo)
foo.to_html(fig, include_plotlyjs=False, full_html=False)
Out[119]:
'<div> <div id="3e14a227-cdf8-49fd-998a-14f5a8c209ce" class="plotly-graph-div" style="height:600px; width:100%;"> </div> <script type="text/javascript"> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("3e14a227-cdf8-49fd-998a-14f5a8c209ce")) { Plotly.newPlot( "3e14a227-cdf8-49fd-998a-14f5a8c209ce", [{"customdata": [[13113], [9432], [6933], [4287], [3120], [3037], [2680], [2545], [2455], [2438], [2380], [2370], [2284], [2257], [2187], [2179], [2169], [2137], [2050], [2004], [1957], [1915], [1896], [1891], [1887], [1878], [1862], [1854], [1850], [1848], [1838], [1832], [1829], [1790], [1695], [1678], [1670], [1655], [1655], [1601], [1591], [1587], [1586], [1586], [1585], [1583], [1560], [1559], [1548], [1537], [1535], [1527], [1523], [1523], [1518], [1511], [1480], [1433], [1429], [1427], [1416], [1416], [1412], [1391], [1374], [1370], [1362], [1353], [1351], [1348], [1346], [1331], [1323], [1308], [1296], [1288], [1286], [1285], [1278], [1272], [1260], [1251], [1248], [1245], [1238], [1234], [1233], [1226], [1210], [1196], [1185], [1182], [1179], [1177], [1176], [1173], [1173], [1170], [1166], [1165]], "hovertemplate": "<b>%{hovertext}</b><br><br>count=%{customdata[0]}<br>Latitude=%{lat}<br>Longitude=%{lon}<extra></extra>", "hovertext": [13113.0, 9432.0, 6933.0, 4287.0, 3120.0, 3037.0, 2680.0, 2545.0, 2455.0, 2438.0, 2380.0, 2370.0, 2284.0, 2257.0, 2187.0, 2179.0, 2169.0, 2137.0, 2050.0, 2004.0, 1957.0, 1915.0, 1896.0, 1891.0, 1887.0, 1878.0, 1862.0, 1854.0, 1850.0, 1848.0, 1838.0, 1832.0, 1829.0, 1790.0, 1695.0, 1678.0, 1670.0, 1655.0, 1655.0, 1601.0, 1591.0, 1587.0, 1586.0, 1586.0, 1585.0, 1583.0, 1560.0, 1559.0, 1548.0, 1537.0, 1535.0, 1527.0, 1523.0, 1523.0, 1518.0, 1511.0, 1480.0, 1433.0, 1429.0, 1427.0, 1416.0, 1416.0, 1412.0, 1391.0, 1374.0, 1370.0, 1362.0, 1353.0, 1351.0, 1348.0, 1346.0, 1331.0, 1323.0, 1308.0, 1296.0, 1288.0, 1286.0, 1285.0, 1278.0, 1272.0, 1260.0, 1251.0, 1248.0, 1245.0, 1238.0, 1234.0, 1233.0, 1226.0, 1210.0, 1196.0, 1185.0, 1182.0, 1179.0, 1177.0, 1176.0, 1173.0, 1173.0, 1170.0, 1166.0, 1165.0], "lat": [41.976290414, 41.754592961, 41.883500187, 41.897895128, 41.896888586, 41.909664252, 41.885487535, 41.904192368, 41.788987036, 41.88233367, 41.721627204, 41.736259984, 41.737094305, 41.68995741, 41.739265865, 41.891990384, 41.868180939, 41.979006297, 41.736148121, 41.706070186, 41.814007401, 42.019399237, 41.868541914, 41.750940757, 41.899410159, 41.766102387, 41.976200173, 41.874363279, 41.929743818, 41.78210152, 41.864493678, 41.876607964, 41.882394062, 41.907153315, 41.908430535, 41.795991039, 41.866810739, 41.88171846, 41.836385231, 41.918711651, 41.903775756, 41.777309867, 41.891694878, 41.707519433, 41.769185486, 41.692833841, 41.73365047, 41.866331725, 41.750832464, 41.878370307, 41.873699424, 41.852216317, 41.742710224, 41.692041778, 41.844112707, 41.71902307, 41.892753031, 41.953436358, 41.946542477, 41.881883933, 41.888165132, 41.902437713, 41.880224549, 41.883210775, 41.839816207, 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-87.62743954], "marker": {"color": "fuchsia"}, "mode": "markers", "name": "", "showlegend": false, "subplot": "mapbox", "type": "scattermapbox"}], {"height": 600, "legend": {"tracegroupgap": 0}, "mapbox": {"center": {"lat": 41.843944627229995, "lon": -87.65970842684999}, "domain": {"x": [0.0, 1.0], "y": [0.0, 1.0]}, "style": "open-street-map", "zoom": 10}, "margin": {"b": 0, "l": 0, "r": 0, "t": 0}, "template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": 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In [120]:
import plotly.express as px
fig = px.scatter_mapbox(latlongrcSampleCast.toPandas(), lat="Latitude", lon="Longitude", size="count", color="count",
color_continuous_scale=px.colors.cyclical.IceFire, zoom=9.5, height=600)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
Run for all points...this crashes my machine each time. Let's try for 100, 000 points now instead.
In [121]:
latlongrc = latlongrc.limit(5000)
In [122]:
latlongrcCast = latlongrc.withColumn("Latitude", latlongrc["Latitude"].cast(DoubleType()))
In [123]:
latlongrcCast = latlongrcCast.withColumn("Longitude", latlongrcCast["Longitude"].cast(DoubleType()))
In [124]:
latlongrcCast.printSchema()
root |-- Latitude: double (nullable = true) |-- Longitude: double (nullable = true) |-- count: long (nullable = false)
In [125]:
import plotly.express as px
fig = px.scatter_mapbox(latlongrcCast.toPandas(), lat="Latitude", lon="Longitude", size="count", color="count",
color_continuous_scale=px.colors.cyclical.IceFire, zoom=9.5, height=600)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
In [126]:
import importlib.util
spec = importlib.util.spec_from_file_location("plotly", "/Users/tallamjr/github/forks/plotly.py/packages/python/plotly/plotly/io/_html.py")
foo = importlib.util.module_from_spec(spec)
spec.loader.exec_module(foo)
foo.to_html(fig, include_plotlyjs=False, full_html=False)
Out[126]:
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RDDs¶
RDDs setup¶
In [127]:
# Get sparkContext
sc = spark.sparkContext
In [128]:
sc
Out[128]:
In [129]:
psrdd = sc.textFile('data/police-stations.csv')
psrdd.first()
Out[129]:
'DISTRICT,DISTRICT NAME,ADDRESS,CITY,STATE,ZIP,WEBSITE,PHONE,FAX,TTY,X COORDINATE,Y COORDINATE,LATITUDE,LONGITUDE,LOCATION'
In [130]:
psrddHeader = psrdd.first()
In [131]:
psrddRest = psrdd.filter(lambda line: line!=psrddHeader)
psrddRest.first()
Out[131]:
'Headquarters,Headquarters,3510 S Michigan Ave,Chicago,IL,60653,http://home.chicagopolice.org,,,,1177731.401,1881697.404,41.83070169,-87.62339535,"(41.8307016873, -87.6233953459)"'
How many police stations are there?
In [132]:
psrddRest.map(lambda line: line.split(',')).collect()
Out[132]:
[['Headquarters', 'Headquarters', '3510 S Michigan Ave', 'Chicago', 'IL', '60653', 'http://home.chicagopolice.org', '', '', '', '1177731.401', '1881697.404', '41.83070169', '-87.62339535', '"(41.8307016873', ' -87.6233953459)"'], ['1', 'Central', '1718 S State St', 'Chicago', 'IL', '60616', 'http://home.chicagopolice.org/community/districts/1st-district-central/', '312-745-4290', '312-745-3694', '312-745-3693', '1176569.052', '1891771.704', '41.85837259', '-87.62735617', '"(41.8583725929', ' -87.627356171)"'], ['6', 'Gresham', '7808 S Halsted St', 'Chicago', 'IL', '60620', 'http://home.chicagopolice.org/community/districts/6th-district-gresham/', '312-745-3617', '312-745-3649', '312-745-3639', '1172283.013', '1853022.646', '41.75213684', '-87.64422891', '"(41.7521368378', ' -87.6442289066)"'], ['11', 'Harrison', '3151 W Harrison St', 'Chicago', 'IL', '60612', 'http://home.chicagopolice.org/community/districts/11th-district-harrison/', '312-746-8386', '312-746-4281', '312-746-5151', '1155244.069', '1897148.755', '41.87358229', '-87.70548813', '"(41.8735822883', ' -87.705488126)"'], ['16', 'Jefferson Park', '5151 N Milwaukee Ave', 'Chicago', 'IL', '60630', 'http://home.chicagopolice.org/community/districts/16th-district-jefferson-park/', '312-742-4480', '312-742-4421', '312-742-4423', '1138480.758', '1933660.473', '41.97409445', '-87.76614884', '"(41.9740944511', ' -87.7661488432)"'], ['24', 'Rogers Park', '6464 N Clark St', 'Chicago', 'IL', '60626', 'http://home.chicagopolice.org/community/districts/24th-district-rogers-park/', '312-744-5907', '312-744-6928', '312-744-7603', '1164193.588', '1943199.401', '41.99976348', '-87.67132429', '"(41.9997634842', ' -87.6713242922)"'], ['2', 'Wentworth', '5101 S Wentworth Ave', 'Chicago', 'IL', '60609', 'http://home.chicagopolice.org/community/districts/2nd-district-wentworth/', '312-747-8366', '312-747-5396', '312-747-6656', '1175864.837', '1871153.753', '41.80181109', '-87.63056018', '"(41.8018110912', ' -87.6305601801)"'], ['7', 'Englewood', '1438 W 63rd St', 'Chicago', 'IL', '60636', 'http://home.chicagopolice.org/community/districts/7th-district-englewood/', '312-747-8223', '312-747-6558', '312-747-6652', '1167659.235', '1863005.522', '41.77963154', '-87.66088702', '"(41.7796315359', ' -87.6608870173)"'], ['25', 'Grand Central', '5555 W Grand Ave', 'Chicago', 'IL', '60639', 'http://home.chicagopolice.org/community/districts/25th-district-grand-central/', '312-746-8605', '312-746-4353', '312-746-8383', '1138770.871', '1913442.439', '41.91860889', '-87.76557448', '"(41.9186088912', ' -87.765574479)"'], ['10', 'Ogden', '3315 W Ogden Ave', 'Chicago', 'IL', '60623', 'http://home.chicagopolice.org/community/districts/10th-district-ogden/', '312-747-7511', '312-747-7429', '312-747-7471', '1154500.753', '1890985.501', '41.85668453', '-87.70838196', '"(41.8566845327', ' -87.708381958)"'], ['15', 'Austin', '5701 W Madison St', 'Chicago', 'IL', '60644', 'http://home.chicagopolice.org/community/districts/15th-district-austin/', '312-743-1440', '312-743-1366', '312-743-1485', '1138148.815', '1899399.078', '41.88008346', '-87.76819989', '"(41.8800834614', ' -87.768199889)"'], ['3', 'Grand Crossing', '7040 S Cottage Grove Ave', 'Chicago', 'IL', '60637', 'http://home.chicagopolice.org/community/districts/3rd-district-grand-crossing/', '312-747-8201', '312-747-5479', '312-747-9168', '1182739.183', '1858317.732', '41.76643089', '-87.60574786', '"(41.7664308925', ' -87.6057478606)"'], ['19', 'Town Hall', '850 W Addison St', 'Chicago', 'IL', '60613', 'http://home.chicagopolice.org/community/districts/19th-district-town-hall/', '312-744-8320', '312-744-4481', '312-744-8011', '1169730.744', '1924160.317', '41.94740046', '-87.65151202', '"(41.9474004564', ' -87.651512018)"'], ['14', 'Shakespeare', '2150 N California Ave', 'Chicago', 'IL', '60647', 'http://home.chicagopolice.org/community/districts/14th-district-shakespeare/', '312-744-8250', '312-744-2422', '312-744-8260', '1157304.426', '1914481.521', '41.92110332', '-87.69745182', '"(41.9211033246', ' -87.6974518223)"'], ['8', 'Chicago Lawn', '3420 W 63rd St', 'Chicago', 'IL', '60629', 'http://home.chicagopolice.org/community/districts/8th-district-chicago-lawn/', '312-747-8730', '312-747-8545', '312-747-8116', '1154575.242', '1862672.049', '41.77898719', '-87.70886382', '"(41.778987189', ' -87.7088638153)"'], ['4', 'South Chicago', '2255 E 103rd St', 'Chicago', 'IL', '60617', 'http://home.chicagopolice.org/community/districts/4th-district-south-chicago/', '312-747-7581', '312-747-5276', '312-747-9169', '1193131.299', '1837090.265', '41.70793329', '-87.56834912', '"(41.7079332906', ' -87.5683491228)"'], ['20', 'Lincoln', '5400 N Lincoln Ave', 'Chicago', 'IL', '60625', 'http://home.chicagopolice.org/community/districts/20th-district-lincoln/', '312-742-8714', '312-742-8803', '312-742-8841', '1158399.146', '1935788.826', '41.97954951', '-87.69284451', '"(41.9795495131', ' -87.6928445094)"'], ['18', 'Near North', '1160 N Larrabee St', 'Chicago', 'IL', '60610', 'http://home.chicagopolice.org/community/districts/18th-district-near-north/', '312-742-5870', '312-742-5771', '312-742-5773', '1172080.029', '1908086.527', '41.90324165', '-87.64335214', '"(41.9032416531', ' -87.6433521393)"'], ['12', 'Near West', '"1412 S Blue Island Ave'], ['"', 'Chicago', 'IL', '60608', 'http://home.chicagopolice.org/community/districts/12th-district-near-west/', '312-746-8396', '312-746-4248', '312-746-9868', '1168487.845', '1893384.455', '41.86297662', '-87.65697251', '"(41.8629766244', ' -87.6569725149)"'], ['9', 'Deering', '3120 S Halsted St', 'Chicago', 'IL', '60608', 'http://home.chicagopolice.org/community/districts/9th-district-deering/', '312-747-8227', '312-747-5329', '312-747-9172', '1171440.24', '1884085.224', '41.83739443', '-87.64640771', '"(41.8373944311', ' -87.6464077068)"'], ['22', 'Morgan Park', '1900 W Monterey Ave', 'Chicago', 'IL', '60643', 'http://home.chicagopolice.org/community/districts/22nd-district-morgan-park/', '312-745-0710', '312-745-0814', '312-745-0569', '1165825.476', '1830851.333', '41.69143478', '-87.66852039', '"(41.6914347795', ' -87.6685203937)"'], ['5', 'Calumet', '727 E 111th St', 'Chicago', 'IL', '60628', 'http://home.chicagopolice.org/community/districts/5th-district-calumet/', '312-747-8210', '312-747-5935', '312-747-9170', '1183305.427', '1831462.313', '41.69272336', '-87.60450587', '"(41.6927233639', ' -87.6045058667)"'], ['17', 'Albany Park', '4650 N Pulaski Rd', 'Chicago', 'IL', '60630', 'http://home.chicagopolice.org/community/districts/17th-district-albany-park/', '312-742-4410', '312-742-5411', '312-742-5451', '1148843.91', '1930801.058', '41.96605342', '-87.72811456', '"(41.9660534171', ' -87.728114561)"']]
In [133]:
psrddRest.map(lambda line: line.split(',')).count()
Out[133]:
24
Display the District ID, District name, Address and Zip for the police station with District ID 7
In [134]:
psrddRest.filter(lambda line: line.split(',')[0] == 7).collect()
Out[134]:
[]
In [135]:
(psrddRest.filter(lambda line: line.split(',')[0] == 7).
map(lambda line: (line.split(',')[0],
line.split(',')[1],
line.split(',')[2],
line.split(',')[5]
)).collect())
Out[135]:
[]
Police stations 10 and 11 are geographically close to each other. Display the District ID, District name, address and zip code
In [136]:
(psrddRest.filter(lambda line: line.split(',')[0] in ['10', '11']).
map(lambda line: (line.split(',')[1],
line.split(',')[2],
line.split(',')[5])).collect())
Out[136]:
[('Harrison', '3151 W Harrison St', '60612'),
('Ogden', '3315 W Ogden Ave', '60623')]
Visualisation¶
In [137]:
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
df_sample = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv')
df_sample_r = df_sample[df_sample['STNAME'] == 'California']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
colorscale = [
'rgb(193, 193, 193)',
'rgb(239,239,239)',
'rgb(195, 196, 222)',
'rgb(144,148,194)',
'rgb(101,104,168)',
'rgb(65, 53, 132)'
]
fig = ff.create_choropleth(
fips=fips, values=values, scope=['CA', 'AZ', 'Nevada', 'Oregon', ' Idaho'],
binning_endpoints=[14348, 63983, 134827, 426762, 2081313], colorscale=colorscale,
county_outline={'color': 'rgb(255,255,255)', 'width': 0.5}, round_legend_values=True,
legend_title='Population by County', title='California and Nearby States'
)
fig.layout.template = None
fig.show()
In [138]:
help(ff.create_choropleth)
Help on function create_choropleth in module plotly.figure_factory._county_choropleth:
create_choropleth(fips, values, scope=['usa'], binning_endpoints=None, colorscale=None, order=None, simplify_county=0.02, simplify_state=0.02, asp=None, show_hover=True, show_state_data=True, state_outline=None, county_outline=None, centroid_marker=None, round_legend_values=False, exponent_format=False, legend_title='', **layout_options)
Returns figure for county choropleth. Uses data from package_data.
:param (list) fips: list of FIPS values which correspond to the con
catination of state and county ids. An example is '01001'.
:param (list) values: list of numbers/strings which correspond to the
fips list. These are the values that will determine how the counties
are colored.
:param (list) scope: list of states and/or states abbreviations. Fits
all states in the camera tightly. Selecting ['usa'] is the equivalent
of appending all 50 states into your scope list. Selecting only 'usa'
does not include 'Alaska', 'Puerto Rico', 'American Samoa',
'Commonwealth of the Northern Mariana Islands', 'Guam',
'United States Virgin Islands'. These must be added manually to the
list.
Default = ['usa']
:param (list) binning_endpoints: ascending numbers which implicitly define
real number intervals which are used as bins. The colorscale used must
have the same number of colors as the number of bins and this will
result in a categorical colormap.
:param (list) colorscale: a list of colors with length equal to the
number of categories of colors. The length must match either all
unique numbers in the 'values' list or if endpoints is being used, the
number of categories created by the endpoints.
For example, if binning_endpoints = [4, 6, 8], then there are 4 bins:
[-inf, 4), [4, 6), [6, 8), [8, inf)
:param (list) order: a list of the unique categories (numbers/bins) in any
desired order. This is helpful if you want to order string values to
a chosen colorscale.
:param (float) simplify_county: determines the simplification factor
for the counties. The larger the number, the fewer vertices and edges
each polygon has. See
http://toblerity.org/shapely/manual.html#object.simplify for more
information.
Default = 0.02
:param (float) simplify_state: simplifies the state outline polygon.
See http://toblerity.org/shapely/manual.html#object.simplify for more
information.
Default = 0.02
:param (float) asp: the width-to-height aspect ratio for the camera.
Default = 2.5
:param (bool) show_hover: show county hover and centroid info
:param (bool) show_state_data: reveals state boundary lines
:param (dict) state_outline: dict of attributes of the state outline
including width and color. See
https://plot.ly/python/reference/#scatter-marker-line for all valid
params
:param (dict) county_outline: dict of attributes of the county outline
including width and color. See
https://plot.ly/python/reference/#scatter-marker-line for all valid
params
:param (dict) centroid_marker: dict of attributes of the centroid marker.
The centroid markers are invisible by default and appear visible on
selection. See https://plot.ly/python/reference/#scatter-marker for
all valid params
:param (bool) round_legend_values: automatically round the numbers that
appear in the legend to the nearest integer.
Default = False
:param (bool) exponent_format: if set to True, puts numbers in the K, M,
B number format. For example 4000.0 becomes 4.0K
Default = False
:param (str) legend_title: title that appears above the legend
:param **layout_options: a **kwargs argument for all layout parameters
Example 1: Florida::
import plotly.plotly as py
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'] == 'Florida']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
binning_endpoints = list(np.mgrid[min(values):max(values):4j])
colorscale = ["#030512","#1d1d3b","#323268","#3d4b94","#3e6ab0",
"#4989bc","#60a7c7","#85c5d3","#b7e0e4","#eafcfd"]
fig = ff.create_choropleth(
fips=fips, values=values, scope=['Florida'], show_state_data=True,
colorscale=colorscale, binning_endpoints=binning_endpoints,
round_legend_values=True, plot_bgcolor='rgb(229,229,229)',
paper_bgcolor='rgb(229,229,229)', legend_title='Florida Population',
county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
exponent_format=True,
)
Example 2: New England
```
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
NE_states = ['Connecticut', 'Maine', 'Massachusetts',
'New Hampshire', 'Rhode Island']
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'].isin(NE_states)]
colorscale = ['rgb(68.0, 1.0, 84.0)',
'rgb(66.0, 64.0, 134.0)',
'rgb(38.0, 130.0, 142.0)',
'rgb(63.0, 188.0, 115.0)',
'rgb(216.0, 226.0, 25.0)']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
fig = ff.create_choropleth(
fips=fips, values=values, scope=NE_states, show_state_data=True
)
py.iplot(fig, filename='choropleth_new_england')
```
Example 3: California and Surrounding States
```
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'] == 'California']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
colorscale = [
'rgb(193, 193, 193)',
'rgb(239,239,239)',
'rgb(195, 196, 222)',
'rgb(144,148,194)',
'rgb(101,104,168)',
'rgb(65, 53, 132)'
]
fig = ff.create_choropleth(
fips=fips, values=values, colorscale=colorscale,
scope=['CA', 'AZ', 'Nevada', 'Oregon', ' Idaho'],
binning_endpoints=[14348, 63983, 134827, 426762, 2081313],
county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
legend_title='California Counties',
title='California and Nearby States'
)
py.iplot(fig, filename='choropleth_california_and_surr_states_outlines')
```
Example 4: USA
```
import plotly.plotly as py
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/laucnty16.csv'
)
df_sample['State FIPS Code'] = df_sample['State FIPS Code'].apply(
lambda x: str(x).zfill(2)
)
df_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(
lambda x: str(x).zfill(3)
)
df_sample['FIPS'] = (
df_sample['State FIPS Code'] + df_sample['County FIPS Code']
)
binning_endpoints = list(np.linspace(1, 12, len(colorscale) - 1))
colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef",
"#b3d2e9", "#9ecae1", "#85bcdb", "#6baed6", "#57a0ce",
"#4292c6", "#3082be", "#2171b5", "#1361a9", "#08519c",
"#0b4083","#08306b"]
fips = df_sample['FIPS']
values = df_sample['Unemployment Rate (%)']
fig = ff.create_choropleth(
fips=fips, values=values, scope=['usa'],
binning_endpoints=binning_endpoints, colorscale=colorscale,
show_hover=True, centroid_marker={'opacity': 0},
asp=2.9, title='USA by Unemployment %',
legend_title='Unemployment %'
)
py.iplot(fig, filename='choropleth_full_usa')
```